Methods of assigning treatment to breast cancer patients
The present invention relates to genetic markers whose expression is correlated with breast cancer. Specifically, the invention provides sets of markers whose expression patterns can be used to differentiate clinical conditions associated with breast cancer, such as the presence or absence of the estrogen receptor ESR1, and BRCA1 and sporadic tumors, and to provide information on the likelihood of tumor distant metastases within five years of initial diagnosis. The invention relates to methods of using these markers to distinguish these conditions. The invention also provides methods of classifying and treating patients based on prognosis. The invention also relates to kits containing ready-to-use microarrays and computer software for data analysis using the diagnostic, prognostic and statistical methods disclosed herein.
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This application is a continuation-in-part of U.S. application Ser. No. 10/172,118, filed Jun. 14, 2002, which in turn claims benefit of both U.S. Provisional Application No. 60/298,918 filed Jun. 18, 2001 and U.S. Provisional Application No. 60/380,710 filed May 14, 2002, each of which is incorporated by reference herein in its entirety.
This application includes a Sequence Listing submitted on compact disc, recorded on two compact discs, including one duplicate, containing Filename 9301188999.txt, of size 6,480 kb, created Jan. 13, 2003. The sequence listing on the compact discs is incorporated by reference herein in its entirety.
1. FIELD OF THE INVENTIONThe present invention relates to the identification of marker genes useful in the diagnosis and prognosis of breast cancer. More particularly, the invention relates to the identification of a set of marker genes associated with breast cancer, a set of marker genes 5differentially expressed in estrogen receptor (+) versus estrogen receptor (−) tumors, a set of marker genes differentially expressed in BRCA1 versus sporadic tumors, and a set of marker genes differentially expressed in sporadic tumors from patients with good clinical prognosis (i.e., metastasis- or disease-free in at least 5 years of follow-up time since diagnosis) versus patients with poor clinical prognosis (i.e., metastasis or disease occurred within 5 years since diagnosis). For each of the marker sets above, the invention further relates to methods of distinguishing the breast cancer-related conditions. The invention further provides methods for determining the course of treatment of a patient with breast cancer.
2. BACKGROUND OF THE INVENTIONThe increased number of cancer cases reported in the United States, and, indeed, around the world, is a major concern. Currently there are only a handful of treatments available for specific types of cancer, and these provide no guarantee of success. In order to be most effective, these treatments require not only an early detection of the malignancy, but a reliable assessment of the severity of the malignancy.
The incidence of breast cancer, a leading cause of death in women, has been gradually increasing in the United States over the last thirty years. Its cumulative risk is relatively high; 1 in 8 women are expected to develop some type of breast cancer by age 85 in the United States. In fact, breast cancer is the most common cancer in women and the second most common cause of cancer death in the United States. In 1997, it was estimated that 181,000 new cases were reported in the U.S., and that 44,000 people would die of breast cancer (Parker et al., CA Cancer J. Clin. 47:5–27 (1997); Chu et al., J. Nat. Cancer Inst. 88:1571–1579 (1996)). While mechanism of tumorigenesis for most breast carcinomas is largely unknown, there are genetic factors that can predispose some women to developing breast cancer (Miki et al., Science, 266:66–71(1994)). The discovery and characterization of BRCA1 and BRCA2 has recently expanded our knowledge of genetic factors which can contribute to familial breast cancer. Germ-line mutations within these two loci are associated with a 50 to 85% lifetime risk of breast and/or ovarian cancer (Casey, Curr. Opin. Oncol. 9:88–93 (1997); Marcus et al., Cancer 77:697–709 (1996)). Only about 5% to 10% of breast cancers are associated with breast cancer susceptibility genes, BRCA1 and BRCA2. The cumulative lifetime risk of breast cancer for women who carry the mutant BRCA1 is predicted to be approximately 92%, while the cumulative lifetime risk for the non-carrier majority is estimated to be approximately 10%. BRCA1 is a tumor suppressor gene that is involved in DNA repair anc cell cycle control, which are both important for the maintenance of genomic stability. More than 90% of all mutations reported so far result in a premature truncation of the protein product with abnormal or abolished function. The histology of breast cancer in BRCA1 mutation carriers differs from that in sporadic cases, but mutation analysis is the only way to find the carrier. Like BRCA1, BRCA2 is involved in the development of breast cancer, and like BRCA1 plays a role in DNA repair. However, unlike BRCA1, it is not involved in ovarian cancer.
Other genes have been linked to breast cancer, for example c-erb-2 (HER2) and p53 (Beenken et al., Ann. Surg. 233(5):630–638 (2001). Overexpression of c-erb-2 (HER2) and p53 have been correlated with poor prognosis (Rudolph et al., Hum. Pathol. 32(3):311–319 (2001), as has been aberrant expression products of mdm2 (Lukas et al., Cancer Res. 61(7):3212–3219 (2001) and cyclin1 and p27 (Porter & Roberts, International Publication WO98/33450, published Aug. 6, 1998). However, no other clinically useful markers consistently associated with breast cancer have been identified.
Sporadic tumors, those not currently associated with a known germline mutation, constitute the majority of breast cancers. It is also likely that other, non-genetic factors also have a significant effect on the etiology of the disease. Regardless of the cancer's origin, breast cancer morbidity and mortality increases significantly if it is not detected early in its progression. Thus, considerable effort has focused on the early detection of cellular transformation and tumor formation in breast tissue.
A marker-based approach to tumor identification and characterization promises improved diagnostic and prognostic reliability. Typically, the diagnosis of breast cancer requires histopathological proof of the presence of the tumor. In addition to diagnosis, histopathological examinations also provide information about prognosis and selection of treatment regimens. Prognosis may also be established based upon clinical parameters such as tumor size, tumor grade, the age of the patient, and lymph node metastasis.
Diagnosis and/or prognosis may be determined to varying degrees of effectiveness by direct examination of the outside of the breast, or through mammography or other X-ray imaging methods (Jatoi, Am. J. Surg. 177:518–524 (1999)). The latter approach is not without considerable cost, however. Every time a mammogram is taken, the patient incurs a small risk of having a breast tumor induced by the ionizing properties of the radiation used during the test. In addition, the process is expensive and the subjective interpretations of a technician can lead to imprecision. For example, one study showed major clinical disagreements for about one-third of a set of mammograms that were interpreted individually by a surveyed group of radiologists. Moreover, many women find that undergoing a mammogram is a painful experience. Accordingly, the National Cancer Institute has not recommended mammograms for women under fifty years of age, since this group is not as likely to develop breast cancers as are older women. It is compelling to note, however, that while only about 22% of breast cancers occur in women under fifty, data suggests that breast cancer is more aggressive in pre-menopausal women.
In clinical practice, accurate diagnosis of various subtypes of breast cancer is important because treatment options, prognosis, and the likelihood of therapeutic response all vary broadly depending on the diagnosis. Accurate prognosis, or determination of distant metastasis-free survival could allow the oncologist to tailor the administration of adjuvant chemotherapy, with women having poorer prognoses being given the most aggressive treatment. Furthermore, accurate prediction of poor prognosis would greatly impact clinical trials for new breast cancer therapies, because potential study patients could then be stratified according to prognosis. Trials could then be limited to patients having poor prognosis, in turn making it easier to discern if an experimental therapy is efficacious.
To date, no set of satisfactory predictors for prognosis based on the clinical information alone has been identified. The detection of BRCA1 or BRCA2 mutations represents a step towards the design of therapies to better control and prevent the appearance of these tumors. However, there is no equivalent means for the diagnosis of patients with sporadic tumors, the most common type of breast cancer tumor, nor is there a means of differentiating subtypes of breast cancer.
Adjuvant systemic therapy has been shown to substantially improve the disease-free and overall survival in both premenopausal and postmenopausal women up to age 70 with lymph node negative and lymph node positive breast cancer. See Early Breast Cancer Trialists' Collaborative Group, Lancet 352(9132):930–942 (1998); Early Breast Cancer Trialists' Collaborative Group, Lancet 351(9114):1451–1467 (1998). The absolute benefit from adjuvant treatment is larger for patients with poor prognostic features and this has resulted in the policy to select only these so-called ‘high-risk’ patients for adjuvant chemotherapy. Goldhirsch et al., Meeting highlights: International Consensus Panel on the Treatment of Primary Breast Cancer, Seventh International Conference on Adjuvant Therapy of Primary Breast Cancer, J. Clin. Oncol. 19(18):3817–3827 (2001); Eifel et al., National Institutes of Health Consensus Development Conference Statement: Adjuvant Therapy for Breast Cancer, Nov. 1–3, 2000, J. Natl. Cancer Inst. 93(13):979–989 (2001). Accepted prognostic and predictive factors in breast cancer include age, tumor size, axillary lymph node status, histological tumor type, pathological grade and hormone receptor status. A large number of other factors has been investigated for their potential to predict disease outcome, but these have in general only limited predictive power. Isaacs et al., Semin. Oncol. 28(1):53–67 (2001).
Using gene expression profiling with cDNA microarrays, Perou et al. showed that there are several subgroups of breast cancer patients based on unsupervised cluster analysis: those of “basal type” and those of “luminal type.” Perou et al., Nature 406(6797):747–752 (2000). These subgroups differ with respect to outcome of disease in patients with locally advanced breast cancer. Sorlie et al., Proc. Natl. Acad. Sci. U.S.A. 98(19):10869–10874 (2001). In addition, microarray analysis has been used to identify diagnostic categories, e.g., BRCA1 and 2 (Hedenfalk et al., N. Engl. J. Med. 344(8):539–548 (2001); van't Veer et al., Nature 415(6871):530–536 (2002)); estrogen receptor (Perou, supra; van't Veer, supra; Gruvberger et al., Cancer. Res. 61(16):5979–5984 (2001)) and lymph node status (West et al., Proc. Natl. Acad. Sci. U.S.A. 98(20):11462–11467 (2001); Ahr et al., Lancet 359(9301):131–132 (2002)).
3. SUMMARY OF THE INVENTIONThe invention provides gene marker sets that distinguish various types and subtypes of breast cancer, and methods of use therefor. In one embodiment, the invention provides a method for classifying a cell sample as ER(+) or ER(−) comprising detecting a difference in the expression of a first plurality of genes relative to a control, said first plurality of genes consisting of at least 5 of the genes corresponding to the markers listed in Table 1. In specific embodiments, said plurality of genes consists of at least 50, 100, 200, 500, 1000, up to 2,460 of the gene markers listed in Table 1. In another specific embodiment, said plurality of genes consists of each of the genes corresponding to the 2,460 markers listed in Table 2. In another specific embodiment, said plurality consists of the 550 markers listed in Table 2. In another specific embodiment, said control comprises nucleic acids derived from a pool of tumors from individual sporadic patients. In another specific embodiment, said detecting comprises the steps of: (a) generating an ER(+) template by hybridization of nucleic acids derived from a plurality of ER(+) patients within a plurality of sporadic patients against nucleic acids derived from a pool of tumors from individual sporadic patients; (b) generating an ER(−) template by hybridization of nucleic acids derived from a plurality of ER(−) patients within said plurality of sporadic patients against nucleic acids derived from said pool of tumors from individual sporadic patients within said plurality; (c) hybridizing nucleic acids derived from an individual sample against said pool; and (d) determining the similarity of marker gene expression in the individual sample to the ER(+) template and the ER(−) template, wherein if said expression is more similar to the ER(+) template, the sample is classified as ER(+), and if said expression is more similar to the ER(−) template, the sample is classified as ER(−).
The invention further provides the above methods, applied to the classification of samples as BRCA1 or sporadic, and classifying patients as having good prognosis or poor prognosis. For the BRCA1/sporadic gene markers, the invention provides that the method may be used wherein the plurality of genes is at least 5, 20, 50, 100, 200 or 300 of the BRCA1/sporadic markers listed in Table 3. In a specific embodiment, the optimum 100 markers listed in Table 4 are used. For the prognostic markers, the invention provides that at least 5, 20, 50, 100, or 200 gene markers listed in Table 5 may be used. In a specific embodiment, the optimum 70 markers listed in Table 6 are used.
The invention further provides that markers may be combined. Thus, in one embodiment, at least 5 markers from Table 1 are used in conjunction with at least 5 markers from Table 3. In another embodiment, at least 5 markers from Table 5 are used in conjunction with at least 5 markers from Table 3. In another embodiment, at least 5 markers from Table 1 are used in conjunction with at least 5 markers from Table 5. In another embodiment, at least 5 markers from each of Tables 1, 3, and 5 are used simultaneously.
The invention further provides a method for classifying a sample as ER(+) or ER(−) by calculating the similarity between the expression of at least 5 of the markers listed in Table 1 in the sample to the expression of the same markers in an ER(−) nucleic acid pool and an ER(+) nucleic acid pool, comprising the steps of: (a) labeling nucleic acids derived from a sample, with a first fluorophore to obtain a first pool of fluorophore-labeled nucleic acids; (b) labeling with a second fluorophore a first pool of nucleic acids derived from two or more ER(+) samples, and a second pool of nucleic acids derived from two or more ER(−) samples; (c) contacting said first fluorophore-labeled nucleic acid and said first pool of second fluorophore-labeled nucleic acid with said first microarray under conditions such that hybridization can occur, and contacting said first fluorophore-labeled nucleic acid and said second pool of second fluorophore-labeled nucleic acid with said second microarray under conditions such that hybridization can occur, detecting at each of a plurality of discrete loci on the first microarray a first flourescent emission signal from said first fluorophore-labeled nucleic acid and a second fluorescent emission signal from said first pool of second fluorophore-labeled genetic matter that is bound to said first microarray under said conditions, and detecting at each of the marker loci on said second microarray said first fluorescent emission signal from said first fluorophore-labeled nucleic acid and a third fluorescent emission signal from said second pool of second fluorophore-labeled nucleic acid; (d) determining the similarity of the sample to the ER(−) and ER(+) pools by comparing said first fluorescence emission signals and said second fluorescence emission signals, and said first emission signals and said third fluorescence emission signals; and (e) classifying the sample as ER(+) where the first fluorescence emission signals are more similar to said second fluorescence emission signals than to said third fluorescent emission signals, and classifying the sample as ER(−) where the first fluorescence emission signals are more similar to said third fluorescence emission signals than to said second fluorescent emission signals, wherein said similarity is defined by a statistical method. The invention further provides that the other disclosed marker sets may be used in the above method to distinguish BRCA1 from sporadic tumors, and patients with poor prognosis from patients with good prognosis.
In a specific embodiment, said similarity is calculated by determining a first sum of the differences of expression levels for each marker between said first fluorophore-labeled nucleic acid and said first pool of second fluorophore-labeled nucleic acid, and a second sum of the differences of expression levels for each marker between said first fluorophore-labeled nucleic acid and said second pool of second fluorophore-labeled nucleic acid, wherein if said first sum is greater than said second sum, the sample is classified as ER(−), and if said second sum is greater than said first sum, the sample is classified as ER(+). In another specific embodiment, said similarity is calculated by computing a first classifier parameter P1 between an ER(+) template and the expression of said markers in said sample, and a second classifier parameter P2 between an ER(−) template and the expression of said markers in said sample, wherein said P1 and P2 are calculated according to the formula:
Pi=({right arrow over (z)}i·{right arrow over (y)})/(∥{right arrow over (z)}i∥·∥{right arrow over (y)}∥), Equation (1)
wherein {right arrow over (z)}1 and {right arrow over (z)}2 are ER(−) and ER(+) templates, respectively, and are calculated by averaging said second fluorescence emission signal for each of said markers in said first pool of second fluorophore-labeled nucleic acid and said third fluorescence emission signal for each of said markers in said second pool of second fluorophore-labeled nucleic acid, respectively, and wherein {right arrow over (y)} is said first fluorescence emission signal of each of said markers in the sample to be classified as ER(+) or ER(−), wherein the expression of the markers in the sample is similar to ER(+) if P1<P2, and similar to ER(−) if P1>P2.
The invention further provides a method for identifying marker genes the expression of which is associated with a particular phenotype. In one embodiment, the invention provides a method for determining a set of marker genes whose expression is associated with a particular phenotype, comprising the steps of: (a) selecting the phenotype having two or more phenotype categories; (b) identifying a plurality of genes wherein the expression of said genes is correlated or anticorrelated with one of the phenotype categories, and wherein the correlation coefficient for each gene is calculated according to the equation
ρ=({right arrow over (c)}·{right arrow over (r)})/(∥{right arrow over (c)}∥·∥{right arrow over (r)}∥) Equation (2)
wherein {right arrow over (c)} is a number representing said phenotype category and {right arrow over (r)} is the logarithmic expression ratio across all the samples for each individual gene, wherein if the correlation coefficient has an absolute value of a threshold value or greater, said expression of said gene is associated with the phenotype category, and wherein said plurality of genes is a set of marker genes whose expression is associated with a particular phenotype. The threshold depends upon the number of samples used; the threshold can be calculated as 3×1/√{square root over (n−3)}, where 1/√{square root over (n−3)} is the distribution width and n=the number of samples. In a specific embodiment where n=98, said threshold value is 0.3. In a specific embodiment, said set of marker genes is validated by: (a) using a statistical method to randomize the association between said marker genes and said phenotype category, thereby creating a control correlation coefficient for each marker gene; (b) repeating step (a) one hundred or more times to develop a frequency distribution of said control correlation coefficients for each marker gene; (c) determining the number of marker genes having a control correlation coefficient of a threshold value or above, thereby creating a control marker gene set; and (d) comparing the number of control marker genes so identified to the number of marker genes, wherein if the p value of the difference between the number of marker genes and the number of control genes is less than 0.01, said set of marker genes is validated. In another specific embodiment, said set of marker genes is optimized by the method comprising: (a) rank-ordering the genes by amplitude of correlation or by significance of the correlation coefficients, and (b) selecting an arbitrary number of marker genes from the top of the rank-ordered list. The threshold value depends upon the number of samples tested.
The invention further provides a method for assigning a person to one of a plurality of categories in a clinical trial, comprising determining for each said person the level of expression of at least five of the prognosis markers listed in Table 6, determining therefrom whether the person has an expression pattern that correlates with a good prognosis or a poor prognosis, and assigning said person to one category in a clinical trial if said person is determined to have a good prognosis, and a different category if that person is determined to have a poor prognosis. The invention further provides a method for assigning a person to one of a plurality of categories in a clinical trial, where each of said categories is associated with a different phenotype, comprising determining for each said person the level of expression of at least five markers from a set of markers, wherein said set of markers includes markers associated with each of said clinical categories, determining therefrom whether the person has an expression pattern that correlates with one of the clinical categories, an assigning said person to one of said categories if said person is determined to have a phenotype associated with that category.
The invention further provides a method of classifying a first cell or organism as having one of at least two different phenotypes, said at least two different phenotypes comprising a first phenotype and a second phenotype, said method comprising: (a) comparing the level of expression of each of a plurality of genes in a first sample from the first cell or organism to the level of expression of each of said genes, respectively, in a pooled sample from a plurality of cells or organisms, said plurality of cells or organisms comprising different cells or organisms exhibiting said at least two different phenotypes, respectively, to produce a first compared value; (b) comparing said first compared value to a second compared value, wherein said second compared value is the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having said first phenotype to the level of expression of each of said genes, respectively, in said pooled sample; (c) comparing said first compared value to a third compared value, wherein said third compared value is the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having said second phenotype to the level of expression of each of said genes, respectively, in said pooled sample, (d) optionally carrying out one or more times a step of comparing said first compared value to one or more additional compared values, respectively, each additional compared value being the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having a phenotype different from said first and second phenotypes but included among said at least two different phenotypes, to the level of expression of each of said genes, respectively, in said pooled sample; and (e) determining to which of said second, third and, if present, one or more additional compared values, said first compared value is most similar, wherein said first cell or organism is determined to have the phenotype of the cell or organism used to produce said compared value most similar to said first compared value.
In a specific embodiment of the above method, said compared values are each ratios of the levels of expression of each of said genes. In another specific embodiment, each of said levels of expression of each of said genes in said pooled sample are normalized prior to any of said comparing steps. In another specific embodiment, normalizing said levels of expression is carried out by dividing each of said levels of expression by the median or mean level of expression of each of said genes or dividing by the mean or median level of expression of one or more housekeeping genes in said pooled sample. In a more specific embodiment, said normalized levels of expression are subjected to a log transform and said comparing steps comprise subtracting said log transform from the log of said levels of expression of each of said genes in said sample from said cell or organism. In another specific embodiment, said at least two different phenotypes are different stages of a disease or disorder. In another specific embodiment, said at least two different phenotypes are different prognoses of a disease or disorder. In yet another specific embodiment, said levels of expression of each of said genes, respectively, in said pooled sample or said levels of expression of each of said genes in a sample from said cell or organism characterized as having said first phenotype, said second phenotype, or said phenotype different from said first and second phenotypes, respectively, are stored on a computer.
The invention further provides microarrays comprising the disclosed marker sets. In one embodiment, the invention provides a microarray comprising at least 5 markers derived from any one of Tables 1–6, wherein at least 50% of the probes on the microarray are present in any one of Tables 1–6. In more specific embodiments, at least 60%, 70%, 80%, 90%, 95% or 98% of the probes on said microarray are present in any one of Tables 1–6.
In another embodiment, the invention provides a microarray for distinguishing ER(+) and ER(−) cell samples comprising a positionally-addressable array of polynucleotide probes bound to a support, said polynucleotide probes comprising a plurality of polynucleotide probes of different nucleotide sequences, each of said different nucleotide sequences comprising a sequence complementary and hybridizable to a plurality of genes, said plurality consisting of at least 5 of the genes corresponding to the markers listed in Table 1 or Table 2, wherein at least 50% of the probes on the microarray are present in any one of Table 1 or Table 2. In yet another embodiment, the invention provides a microarray for distinguishing BRCA1-type and sporadic tumor-type cell samples comprising a positionally-addressable array of polynucleotide probes bound to a support, said polynucleotide probes comprising a plurality of polynucleotide probes of different nucleotide sequences, each of said different nucleotide sequences comprising a sequence complementary and hybridizable to a plurality of genes, said plurality consisting of at least 5 of the genes corresponding to the markers listed in Table 3 or Table 4, wherein at least 50% of the probes on the microarray are present in any one of Table 3 or Table 4. In still another embodiment, the invention provides a microarray for distinguishing cell samples from patients having a good prognosis and cell samples from patients having a poor prognosis comprising a positionally-addressable array of polynucleotide probes bound to a support, said polynucleotide probes comprising a plurality of polynucleotide probes of different nucleotide sequences, each of said different nucleotide sequences comprising a sequence complementary and hybridizable to a plurality of genes, said plurality consisting of at least 5 of the genes corresponding to the markers listed in Table 5 or Table 6, wherein at least 50% of the probes on the microarray are present in any one of Table 5 or Table 6. The invention further provides for microarrays comprising at least 5, 20, 50, 100, 200, 500, 100, 1,250, 1,500, 1,750, or 2,000 of the ER-status marker genes listed in Table 1, at least 5, 20, 50, 100, 200, or 300 of the BRCA1 sporadic marker genes listed in Table 3, or at least 5, 20, 50, 100 or 200 of the prognostic marker genes listed in Table 5, in any combination, wherein at least 50%, 60%, 70%, 80%, 90%, 95% or 98% of the probes on said microarrays are present in Table 1, Table 3 and/or Table 5.
The invention further provides a kit for determining the ER-status of a sample, comprising at least two microarrays each comprising at least 5 of the markers listed in Table 1, and a computer system for determining the similarity of the level of nucleic acid derived from the markers listed in Table 1 in a sample to that in an ER(−) pool and an ER(+) pool, the computer system comprising a processor, and a memory encoding one or more programs coupled to the processor, wherein the one or more programs cause the processor to perform a method comprising computing the aggregate differences in expression of each marker between the sample and ER(−) pool and the aggregate differences in expression of each marker between the sample and ER(+) pool, or a method comprising determining the correlation of expression of the markers in the sample to the expression in the ER(−) and ER(+) pools, said correlation calculated according to Equation (4). The invention provides for kits able to distinguish BRCA1 and sporadic tumors, and samples from patients with good prognosis from samples from patients with poor prognosis, by inclusion of the appropriate marker gene sets. The invention further provides a kit for determining whether a sample is derived from a patient having a good prognosis or a poor prognosis, comprising at least one microarray comprising probes to at least 5 of the genes corresponding to the markers listed in Table 5, and a computer readable medium having recorded thereon one or more programs for determining the similarity of the level of nucleic acid derived from the markers listed in Table 5 in a sample to that in a pool of samples derived from individuals having a good prognosis and a pool of samples derived from individuals having a good prognosis, wherein the one or more programs cause a computer to perform a method comprising computing the aggregate differences in expression of each marker between the sample and the good prognosis pool and the aggregate differences in expression of each marker between the sample and the poor prognosis pool, or a method comprising determining the correlation of expression of the markers in the sample to the expression in the good prognosis and poor prognosis pools, said correlation calculated according to Equation (3).
The invention further provides a method for classifying a breast cancer patient according to prognosis, comprising: (a) comparing the respective levels of expression of at least five genes for which markers are listed in Table 5 in a cell sample taken from said breast cancer patient to respective control levels of expression of said at least five genes; and (b) classifying said breast cancer patient according to prognosis of his or her breast cancer based on the similarity between said levels of expression in said cell sample and said control levels. In a specific embodiment of this method, step (b) comprises determining whether said similarity exceeds one or more predetermined threshold values of similarity. In another more specific embodiment of this method, said control levels are the mean levels of expression of each of said at least five genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have no distant metastases within five years of initial diagnosis. In another specific embodiment of this method, said control levels comprise the expression levels of said genes in breast cancer patients who have had no distant metastases within five years of initial diagnosis. In another specific embodiment of this method, said control levels comprise, for each of said at least five genes, mean log intensity values stored on a computer. In another specific embodiment of this method, said control levels comprise, for each of said at least five genes, the mean log intensity values that are listed in Table 7. In another specific embodiment of this method, said comparing step (a) comprises comparing the respective levels of expression of at least ten of said genes for which markers are listed in Table 5 in said cell sample to said respective control levels of said at least ten of said genes, wherein said control levels of expression of said at least ten genes are the average expression levels of each of said at least ten genes in a pool of tumor samples obtained from breast cancer patients who have had no distant metastases within five years of initial diagnosis. In another specific embodiment of this method, said comparing step (a) comprises comparing the respective levels of expression of at least 25 of said genes for which markers are listed in Table 5 in said cell sample to said respective control levels of expression of said at least 25 genes, wherein said control levels of expression of said at least 25 genes are the average expression levels of each of said at least 25 genes in a pool of tumor samples obtained from breast cancer patients who have had no distant metastases within five years of initial diagnosis. In another specific embodiment of this method, said comparing step (a) comprises comparing the respective levels of expression of each of said genes for which markers are listed in Table 6 in said cell sample to said respective control levels of expression of each of said genes for which markers are listed in Table 6, wherein said control levels of expression of each of said genes for which markers are listed in Table 6 are the average expression levels of each of said genes in a pool of tumor samples obtained from breast cancer patients who have had no distant metastases within five years of initial diagnosis.
The invention further provides for a method for classifying a breast cancer patient according to prognosis, comprising: (a) determining the similarity between the level of expression of each of at least five genes for which markers are listed in Table 5 in a cell sample taken from said breast cancer patient, to control levels of expression for each respective said at least five genes to obtain a patient similarity value; (b) providing selected first and second threshold values of similarity of said level of expression of each of said at least five genes to said control levels of expression to obtain first and second similarity threshold values, respectively, wherein said second similarity threshold indicates greater similarity to said control than does said first similarity threshold; and (c) classifying said breast cancer patient as having a first prognosis if said patient similarity value exceeds said first and said second similarity threshold values, a second prognosis if said level of expression of said genes exceeds said first similarity threshold value but does not exceed said second similarity threshold value, and a third prognosis if said level of expression of said genes does not exceed said first similarity threshold value or said second similarity threshold value. A specific embodiment of this method comprises determining, prior to step (a), said level of expression of said at least five genes. In another specific embodiment of this method, said determining in step (a) is carried out by a method comprising determining the degree of similarity between the level of expression of each of said at least five genes in a sample taken from said breast cancer patient to the level of expression of each of said at least five genes in a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis. In another specific embodiment of this method, said determining in step (a) is carried out by a method comprising determining the difference between the absolute expression level of each of said at least five genes and the average expression level of each of said at least five genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis. In another specific embodiment of this method, said first threshold value and said second threshold value are coefficients of correlation to the mean expression level of each of said at least five genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis. In a more specific embodiment of this method, said first threshold similarity value and said second threshold similarity values are selected by a method comprising: (a) rank ordering in descending order said tumor samples that compose said pool of tumor samples by the degree of similarity between the level of expression of each said at least five genes in each of said tumor samples to the mean level of expression of said at least five genes of the remaining tumor samples that compose said pool to obtain a rank-ordered list, said degree of similarity being expressed as a similarity value; (b) determining an acceptable number of false negatives in said classifying step, wherein a false negative is a breast cancer patient for whom the expression levels of said at least five genes in said cell sample predicts that said breast cancer patient will have no distant metastases within the first five years after initial diagnosis, but who has had a distant metastasis within the first five years after initial diagnosis; (c) determining a similarity value above which in said rank ordered list fewer than said acceptable number of tumor samples are false negatives; (d) selecting said similarity value determined in step (c) as said first threshold similarity value; and (e) selecting a second similarity value, greater than said first similarity value, as said second threshold similarity value. In an even more specific embodiment of this method, said second threshold similarity value is selected in step (e) by a method comprising determining which of said tumor samples, taken from said breast cancer patients having a distant metastasis within the first five years after initial diagnosis, in said rank ordered list has the greatest similarity value, and selecting said greatest similarity value as said second threshold similarity value. In another even more specific embodiment of this method, said first and second threshold similarity values are correlation coefficients, and said first threshold similarity value is 0.4 and said second threshold similarity value is greater than 0.4. In another even more specific embodiment of this method, said first and second threshold similarity values are correlation coefficients, and said second threshold similarity value is 0.636.
The invention further provides a method of classifying a breast cancer patient according to prognosis comprising the steps of: (a) contacting first nucleic acids derived from a tumor sample taken from said breast cancer patient, and second nucleic acids derived from two or more tumor samples from breast cancer patients who have had no distant metastases within five years of initial diagnosis, with an array under conditions such that hybridization can occur, said array comprising a positionally-addressable ordered array of polynucleotide probes bound to a solid support, said polynucleotide probes being complementary and hybridizable to at least five of the genes respectively for which markers are listed in Table 5, or the RNA encoded by said genes, and wherein at least 50% of the probes on said array are hybridizable to genes respectively for which markers are listed in Table 5, or to the RNA encoded by said genes; (b) detecting at each of a plurality of discrete loci on said array a first fluorescent emission signal from said first nucleic acids and a second fluorescent emission signal from said second nucleic acids that are bound to said array under said conditions; (c) calculating the similarity between said first fluorescent emission signals and said second fluorescent emission signals across said at least five genes respectively for which markers are listed in Table 5; and (d)classifying said breast cancer patient according to prognosis of his or her breast cancer based on the similarity between said first fluorescent emission signals and said second fluorescent emission signals across said at least five genes respectively for which markers are listed in Table 5.
The invention further provides for methods of assigning therapeutic regimen to breast cancer patients. In one embodiment, the invention provides a method of assigning a therapeutic regimen to a breast cancer patient, comprising: (a) classifying said patient as having a “poor prognosis,” “intermediate prognosis,” or “very good prognosis” on the basis of the levels of expression of at least five genes for which markers are listed in Table 5; and (b) assigning said patient a therapeutic regimen, said therapeutic regimen (i) comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or (ii) comprising chemotherapy if said patient has any other combination of lymph node status and expression profile.
The invention also provides a method of assigning a therapeutic regimen to a breast cancer patient, comprising: (a) determining the lymph node status for said patient; (b) determining the level of expression of at least five genes for which markers are listed in Table 5 in a cell sample from said patient, thereby generating an expression profile; (c) classifying said patient as having a “poor prognosis,” “intermediate prognosis,” or “very good prognosis” on the basis of said expression profile; and (d) assigning said patient a therapeutic regimen, said therapeutic regimen comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or comprising chemotherapy if said patient has any other combination of lymph node status and classification. In a specific embodiment of this method, said therapeutic regimen assigned to lymph node negative patients classified as having an “intermediate prognosis” additionally comprises adjuvant hormonal therapy. In another specific embodiment of this method, said classifying step (c) is carried out by a method comprising: (a) rank ordering in descending order a plurality of breast cancer tumor samples that compose a pool of breast cancer tumor samples by the degree of similarity between the level of expression of said at least five genes in each of said tumor samples and the level of expression of said at least five genes across all remaining tumor samples that compose said pool, said degree of similarity being expressed as a similarity value; (b) determining an acceptable number of false negatives in said classifying step, wherein a false negative is a breast cancer patient for whom the expression levels of said at least five genes in said cell sample predicts that said breast cancer patient will have no distant metastases within the first five years after initial diagnosis, but who has had a distant metastasis within the first five years after initial diagnosis; (c) determining a similarity value above which in said rank ordered list said acceptable number of tumor samples or fewer are false negatives; (d) selecting said similarity value determined in step (c) as a first threshold similarity value; (e) selecting a second similarity value, greater than said first similarity value, as a second threshold similarity value; and (f) determining the similarity between the level of expression of each of said at least five genes in a breast cancer tumor sample from the breast cancer patient and the level of expression of each of said respective at least five genes in said pool, to obtain a patient similarity value, wherein if said patient similarity value equals or exceeds said second threshold similarity value, said patient is classified as having a “very good prognosis”; if said patient similarity value equals or exceeds said first threshold similarity value, but is less than said second threshold similarity value, said patient is classified as having an “intermediate prognosis”; and if said patient similarity value is less than said first threshold similarity value, said patient is classified as having a “poor prognosis.” Another specific embodiment of this method comprises determining the estrogen receptor (ER) status of said patient, wherein if said patient is ER positive and lymph node negative, said therapeutic regimen assigned to said patient additionally comprises adjuvant hormonal therapy. In another specific embodiment of this method, said patient is 52 years of age or younger. In another specific embodiment of this method, said patient has stage I or stage II breast cancer. In yet another specific embodiment of this method, said patient is premenopausal.
The above methods may be computer-implemented. Thus, in another embodiment, the invention provides a computer program product for classifying a breast cancer patient according to prognosis, the computer program product for use in conjunction with a computer having a memory and a processor, the computer program product comprising a computer readable storage medium having a computer program encoded thereon, wherein said computer program product can be loaded into the one or more memory units of a computer and causes the one or more processor units of the computer to execute the steps of: (a) receiving a first data structure comprising the respective levels of expression of each of at least five genes for which markers are listed in Table 5 in a cell sample taken from said patient; (b) determining the similarity of the level of expression of each of said at least five genes to respective control levels of expression of said at least five genes to obtain a patient similarity value; (c) comparing said patient similarity value to selected first and second threshold values of similarity of said respective levels of expression of each of said at least five genes to said respective control levels of expression of said at least five genes, wherein said second threshold value of similarity indicates greater similarity to said respective control levels of expression of said at least five genes than does said first threshold value of similarity; and (d) classifying said patient as having a first prognosis if said patient similarity value exceeds said first and said second threshold similarity values; a second prognosis if said patient similarity value exceeds said first threshold similarity value but does not exceed said second threshold similarity value; and a third prognosis if said patient similarity value does not exceed said first threshold similarity value or said second threshold similarity value. In a specific embodiment of the computer program product, said first threshold value of similarity and said second threshold value of similarity are values stored in said computer. In another specific embodiment of the computer program product, said respective control levels of expression of said at least five genes is stored in said computer. In another specific embodiment of the computer program product, said first prognosis is a “very good prognosis”; said second prognosis is an “intermediate prognosis”; and said third prognosis is a “poor prognosis”; wherein said computer program may be loaded into the memory and further cause said one or more processor units of said computer to execute the step of assigning said breast cancer patient a therapeutic regimen comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or comprising chemotherapy if said patient has any other combination of lymph node status and expression profile. In a more specific embodiment, said clinical data includes the lymph node and estrogen receptor (ER) status of said breast cancer patient. In yet another specific embodiment, said computer program may be loaded into the memory and further causes said one or more processor units of the computer to execute the steps of receiving a data structure comprising clinical data specific to said breast cancer patient. In another specific embodiment, said respective control levels of expression of said at least five genes comprises a set of single-channel mean hybridization intensity values for each of said at least five genes, stored on said computer readable storage medium. In a more specific embodiment of this computer program product, said single-channel mean hybridization intensity values are log transformed. In another specific embodiment of the computer program product, said computer program product causes said processing unit to perform said comparing step (c) by calculating the difference between the level of expression of each of said at least five genes in said cell sample taken from said breast cancer patient and said respective control levels of expression of said at least five genes. In another specific embodiment of the computer program product, said computer program product causes said processing unit to perform said comparing step (c) by calculating the mean log level of expression of each of said at least five genes in said control to obtain a control mean log expression level for each gene, calculating the log expression level for each of said at least five genes in a breast cancer sample from said patient to obtain a patient log expression level, and calculating the difference between the patient log expression level and the control mean log expression for each of said at least five genes. In another specific embodiment of the computer program product, said computer program product causes said processing unit to perform said comparing step (c) by calculating similarity between the level of expression of each of said at least five genes in said cell sample taken from said patient and said respective control levels of expression of said at least five genes, wherein said similarity is expressed as a similarity value. In a more specific embodiment of this computer program product, said similarity value is a correlation coefficient.
Squares represent samples from patients with sporadic-type tumors; dots represent samples from patients carrying the BRCA1 mutation.
The invention relates to sets of genetic markers whose expression patterns correlate with important characteristics of breast cancer tumors. i.e., estrogen receptor (ER) status, BRCA1 status, and the likelihood of relapse (i.e., distant metastasis or poor prognosis). More specifically, the invention provides for sets of genetic markers that can distinguish the following three clinical conditions. First, the invention relates to sets of markers whose expression correlates with the ER status of a patient, and which can be used to distinguish ER(+) from ER(−) patients. ER status is a useful prognostic indicator, and an indicator of the likelihood that a patient will respond to certain therapies, such as tamoxifen. Also, among women who are ER positive the response rate (over 50%) to hormonal therapy is much higher than the response rate (less 10%) in patients whose ER status is negative. In patients with ER positive tumors the possibility of achieving a hormonal response is directly proportional to the level ER (P. Calabresi and P. S. Schein, M
As used herein, “BRCA1 tumor” means a tumor having cells containing a mutation of the BRCA1 locus.
The “absolute amplitude” of correlation expressions means the distance, either positive or negative, from a zero value; i.e., both correlation coefficients −0.35 and 0.35 have an absolute amplitude of 0.35.
“Status” means a state of gene expression of a set of genetic markers whose expression is strongly correlated with a particular phenotype. For example, “ER status” means a state of gene expression of a set of genetic markers whose expression is strongly correlated with that of ESR1 (estrogen receptor gene), wherein the pattern of these genes' expression differs detectably between tumors expressing the receptor and tumors not expressing the receptor.
“Good prognosis” means that a patient is expected to have no distant metastases of a breast tumor within five years of initial diagnosis of breast cancer.
“Poor prognosis” means that a patient is expected to have distant metastases of a breast tumor within five years of initial diagnosis of breast cancer.
“Marker” means an entire gene, or an EST derived from that gene, the expression or level of which changes between certain conditions. Where the expression of the gene correlates with a certain condition, the gene is a marker for that condition.
“Marker-derived polynucleotides” means the RNA transcribed from a marker gene, any cDNA or cRNA produced therefrom, and any nucleic acid derived therefrom, such as synthetic nucleic acid having a sequence derived from the gene corresponding to the marker gene.
A “similarity value” is a number that represents the degree of similarity between two things being compared. For example, a similarity value may be a number that indicates the overall similarity between a patient's expression profile using specific phenotype-related markers and a control specific to that phenotype (for instance, the similarity to a “good prognosis” template, where the phenotype is a good prognosis). The similarity value may be expressed as a similarity metric, such as a correlation coefficient, or may simply be expressed as the expression level difference, or the aggregate of the expression level differences, between a patient sample and a template.
5.3 Markers Useful in Diagnosis and Prognosis of Breast Cancer 5.3.1 Marker SetsThe invention provides a set of 4,986 genetic markers whose expression is correlated with the existence of breast cancer by clustering analysis. A subset of these markers identified as useful for diagnosis or prognosis is listed as SEQ ID NOS: 1–2,699. The invention also provides a method of using these markers to distinguish tumor types in diagnosis or prognosis.
In one embodiment, the invention provides a set of 2,460 genetic markers that can classify breast cancer patients by estrogen receptor (ER) status; i.e., distinguish between ER(+) and ER(−) patients or tumors derived from these patients. ER status is an important indicator of the likelihood of a patient's response to some chemotherapies (i.e., tamoxifen). These markers are listed in Table 1. The invention also provides subsets of at least 5, 10, 25, 50, 100, 200, 300, 400, 500, 750, 1,000, 1,250, 1,500, 1,750 or 2,000 genetic markers, drawn from the set of 2,460 markers, which also distinguish ER(+) and ER(−) patients or tumors. Preferably, the number of markers is 550. The invention further provides a set of 550 of the 2,460 markers that are optimal for distinguishing ER status (Table 2). The invention also provides a method of using these markers to distinguish between ER(+) and ER(−) patients or tumors derived therefrom.
In another embodiment, the invention provides a set of 430 genetic markers that can classify ER(−) breast cancer patients by BRCA1 status; i.e., distinguish between tumors containing a BRCA1 mutation and sporadic tumors. These markers are listed in Table 3. The invention further provides subsets of at least 5, 10 20, 30, 40, 50, 75, 100, 150, 200, 250, 300 or 350 markers, drawn from the set of 430 markers, which also distinguish between tumors containing a BRCA1 mutation and sporadic tumors. Preferably, the number of markers is 100. A preferred set of 100 markers is provided in Table 4. The invention also provides a method of using these markers to distinguish between BRCA1 and sporadic patients or tumors derived therefrom.
In another embodiment, the invention provides a set of 231 genetic markers that can distinguish between patients with a good breast cancer prognosis (no breast cancer tumor distant metastases within five years) and patients with a poor breast cancer prognosis (tumor distant metastases within five years). These markers are listed in Table 5. The invention also provides subsets of at least 5, 10, 20, 30, 40, 50, 75, 100, 150 or 200 markers, drawn from the set of 231, which also distinguish between patients with good and poor prognosis. A preferred set of 70 markers is provided in Table 6. In a specific embodiment, the set of markers consists of the twelve kinase-related markers and the seven cell division- or mitosis-related markers listed. The invention also provides a method of using the above markers to distinguish between patients with good or poor prognosis. In another embodiment, the invention provides a method of using the prognosis-associated markers to distinguish between patients having a very good prognosis, an intermediate prognosis, and a poor prognosis, and thereby determining the appropriate combination of adjuvant or hormonal therapy.
The sets of markers listed in Tables 1–6 partially overlap; in other words, some markers are present in multiple sets, while other markers are unique to a set (
Any of the sets of markers provided above may be used alone specifically or in combination with markers outside the set. For example, markers that distinguish ER-status may be used in combination with the BRCA1 vs. sporadic markers, or with the prognostic markers, or both. Any of the marker sets provided above may also be used in combination with other markers for breast cancer, or for any other clinical or physiological condition.
The relationship between the marker sets is diagramed in
The present invention provides sets of markers for the identification of conditions or indications associated with breast cancer. Generally, the marker sets were identified by determining which of ˜25,000 human markers had expression patters that correlated with the conditions or indications.
In one embodiment, the method for identifying marker sets is as follows. After extraction and labeling of target polynucleotides, the expression of all markers (genes) in a sample X is compared to the expression of all markers in a standard or control. In one embodiment, the standard or control comprises target polynucleotide molecules derived from a sample from a normal individual (i.e., an individual not afflicted with breast cancer). In a preferred embodiment, the standard or control is a pool of target polynucleotide molecules. The pool may derived from collected samples from a number of normal individuals. In a preferred embodiment, the pool comprises samples taken from a number of individuals having sporadic-type tumors. In another preferred embodiment, the pool comprises an artificially-generated population of nucleic acids designed to approximate the level of nucleic acid derived from each marker found in a pool of marker-derived nucleic acids derived from tumor samples. In yet another embodiment, the pool is derived from normal or breast cancer cell lines or cell line samples.
The comparison may be accomplished by any means known in the art. For example, expression levels of various markers may be assessed by separation of target polynucleotide molecules (e.g., RNA or cDNA) derived from the markers in agarose or polyacrylamide gels, followed by hybridization with marker-specific oligonucleotide probes. Alternatively, the comparison may be accomplished by the labeling of target polynucleotide molecules followed by separation on a sequencing gel. Polynucleotide samples are placed on the gel such that patient and control or standard polynucleotides are in adjacent lanes. Comparison of expression levels is accomplished visually or by means of densitometer. In a preferred embodiment, the expression of all markers is assessed simultaneously by hybridization to a microarray. In each approach, markers meeting certain criteria are identified as associated with breast cancer.
A marker is selected based upon significant difference of expression in a sample as compared to a standard or control condition. Selection may be made based upon either significant up- or down regulation of the marker in the patient sample. Selection may also be made by calculation of the statistical significance (i.e., the p-value) of the correlation between the expression of the marker and the condition or indication. Preferably, both selection criteria are used. Thus, in one embodiment of the present invention, markers associated with breast cancer are selected where the markers show both more than two-fold change (increase or decrease) in expression as compared to a standard, and the p-value for the correlation between the existence of breast cancer and the change in marker expression is no more than 0.01 (i.e., is statistically significant).
The expression of the identified breast cancer-related markers is then used to identify markers that can differentiate tumors into clinical types. In a specific embodiment using a number of tumor samples, markers are identified by calculation of correlation coefficients between the clinical category or clinical parameter(s) and the linear, logarithmic or any transform of the expression ratio across all samples for each individual gene. Specifically, the correlation coefficient is calculated as
ρ=({right arrow over (c)}·{right arrow over (r)})/(∥{right arrow over (c)}∥·∥{right arrow over (r)}∥) Equation (2)
where {right arrow over (c)} represents the clinical parameters or categories and {right arrow over (r)} represents the linear, logarithmic or any transform of the ratio of expression between sample and control. Markers for which the coefficient of correlation exceeds a cutoff are identified as breast cancer-related markers specific for a particular clinical type. Such a cutoff or threshold corresponds to a certain significance of discriminating genes obtained by Monte Carlo simulations. The threshold depends upon the number of samples used; the threshold can be calculated as 3×1/√{square root over (n−3)}, where 1/√{square root over (n−3)} is the distribution width and n=the number of samples. In a specific embodiment, markers are chosen if the correlation coefficient is greater than about 0.3 or less than about −0.3.
Next, the significance of the correlation is calculated. This significance may be calculated by any statistical means by which such significance is calculated. In a specific example, a set of correlation data is generated using a Monte-Carlo technique to randomize the association between the expression difference of a particular marker and the clinical category. The frequency distribution of markers satisfying the criteria through calculation of correlation coefficients is compared to the number of markers satisfying the criteria in the data generated through the Monte-Carlo technique. The frequency distribution of markers satisfying the criteria in the Monte-Carlo runs is used to determine whether the number of markers selected by correlation with clinical data is significant. See Example 4.
Once a marker set is identified, the markers may be rank-ordered in order of significance of discrimination. One means of rank ordering is by the amplitude of correlation between the change in gene expression of the marker and the specific condition being discriminated. Another, preferred, means is to use a statistical metric. In a specific embodiment, the metric is a Fisher-like statistic:
t=(<x1>−<x2>)/√{square root over ([σ12(n1−1)+σ22(n2−1)]/(n1+n2−1)/(1/n1+1/n2))}{square root over ([σ12(n1−1)+σ22(n2−1)]/(n1+n2−1)/(1/n1+1/n2))}{square root over ([σ12(n1−1)+σ22(n2−1)]/(n1+n2−1)/(1/n1+1/n2))}{square root over ([σ12(n1−1)+σ22(n2−1)]/(n1+n2−1)/(1/n1+1/n2))} Equation (3)
In this equation, <x1> is the error-weighted average of the log ratio of transcript expression measurements within a first diagnostic group (e.g., ER(−), <x2> is the error-weighted average of log ratio within a second, related diagnostic group (e.g., ER(+)), σ1 is the variance of the log ratio within the ER(−) group and n1 is the number of samples for which valid measurements of log ratios are available. σ2 is the variance of log ratio within the second diagnostic group (e.g., ER(+)), and n2 is the number of samples for which valid measurements of log ratios are available. The t-value represents the variance-compensated difference between two means.
The rank-ordered marker set may be used to optimize the number of markers in the set used for discrimination. This is accomplished generally in a “leave one out” method as follows. In a first run, a subset, for example 5, of the markers from the top of the ranked list is used to generate a template, where out of X samples, X-1 are used to generate the template, and the status of the remaining sample is predicted. This process is repeated for every sample until every one of the X samples is predicted once. In a second run, additional markers, for example 5, are added, so that a template is now generated from 10 markers, and the outcome of the remaining sample is predicted. This process is repeated until the entire set of markers is used to generate the template. For each of the runs, type 1 error (false negative) and type 2 errors (false positive) are counted; the optimal number of markers is that number where the type 1 error rate, or type 2 error rate, or preferably the total of type 1 and type 2 error rate is lowest.
For prognostic markers, validation of the marker set may be accomplished by an additional statistic, a survival model. This statistic generates the probability of tumor distant metastases as a function of time since initial diagnosis. A number of models may be used, including Weibull, normal, log-normal, log logistic, log-exponential, or log-Rayleigh (Chapter 12 “Life Testing”, S-PLUS 2000 G
P=α× exp(−t2/τ2) Equation (4)
where α is fixed and equal to 1, and τ is a parameter to be fitted and measures the “expected lifetime”.
It will be apparent to those skilled in the art that the above methods, in particular the statistical methods, described above, are not limited to the identification of markers associated with breast cancer, but may be used to identify set of marker genes associated with any phenotype. The phenotype can be the presence or absence of a disease such as cancer, or the presence or absence of any identifying clinical condition associated with that cancer. In the disease context, the phenotype may be a prognosis such as a survival time, probability of distant metastases of a disease condition, or likelihood of a particular response to a therapeutic or prophylactic regimen. The phenotype need not be cancer, or a disease; the phenotype may be a nominal characteristic associated with a healthy individual.
5.3.3 Sample CollectionIn the present invention, target polynucleotide molecules are extracted from a sample taken from an individual afflicted with breast cancer. The sample may be collected in any clinically acceptable manner, but must be collected such that marker-derived polynucleotides (i.e., RNA) are preserved. mRNA or nucleic acids derived therefrom (i.e., cDNA or amplified DNA) are preferably labeled distinguishably from standard or control polynucleotide molecules, and both are simultaneously or independently hybridized to a microarray comprising some or all of the markers or marker sets or subsets described above. Alternatively, mRNA or nucleic acids derived therefrom may be labeled with the same label as the standard or control polynucleotide molecules, wherein the intensity of hybridization of each at a particular probe is compared. A sample may comprise any clinically relevant tissue sample, such as a tumor biopsy or fine needle aspirate, or a sample of bodily fluid, such as blood, plasma, serum, lymph, ascitic fluid, cystic fluid, urine or nipple exudate. The sample may be taken from a human, or, in a veterinary context, from non-human animals such as ruminants, horses, swine or sheep, or from domestic companion animals such as felines and canines.
Methods for preparing total and poly(A)+RNA are well known and are described generally in Sambrook et al., M
RNA may be isolated from eukaryotic cells by procedures that involve lysis of the cells and denaturation of the proteins contained therein. Cells of interest include wild-type cells (i.e., non-cancerous), drug-exposed wild-type cells, tumor- or tumor-derived cells, modified cells, normal or tumor cell line cells, and drug-exposed modified cells.
Additional steps may be employed to remove DNA. Cell lysis may be accomplished with a nonionic detergent, followed by microcentrifugation to remove the nuclei and hence the bulk of the cellular DNA. In one embodiment, RNA is extracted from cells of the various types of interest using guanidinium thiocyanate lysis followed by CsCl centrifugation to separate the RNA from DNA (Chirgwin et al., Biochemistry 18:5294–5299 (1979)). Poly(A)+RNA is selected by selection with oligo-dT cellulose (see Sambrook et al., M
If desired, RNAse inhibitors may be added to the lysis buffer. Likewise, for certain cell types, it may be desirable to add a protein denaturation/digestion step to the protocol.
For many applications, it is desirable to preferentially enrich mRNA with respect to other cellular RNAs, such as transfer RNA (tRNA) and ribosomal RNA (rRNA). Most mRNAs contain a poly(A) tail at their 3′ end. This allows them to be enriched by affinity chromatography, for example, using oligo(dT) or poly(U) coupled to a solid support, such as cellulose or SEPHADEX® medium (see Ausubel et al., C
The sample of RNA can comprise a plurality of different mRNA molecules, each different mRNA molecule having a different nucleotide sequence. In a specific embodiment, the mRNA molecules in the RNA sample comprise at least 100 different nucleotide sequences. More preferably, the mRNA molecules of the RNA sample comprise mRNA molecules corresponding to each of the marker genes. In another specific embodiment, the RNA sample is a mammalian RNA sample.
In a specific embodiment, total RNA or mRNA from cells are used in the methods of the invention. The source of the RNA can be cells of a plant or animal, human, mammal, primate, non-human animal, dog, cat, mouse, rat, bird, yeast, eukaryote, prokaryote, etc. In specific embodiments, the method of the invention is used with a sample containing total mRNA or total RNA from 1×106 cells or less. In another embodiment, proteins can be isolated from the foregoing sources, by methods known in the art, for use in expression analysis at the protein level.
Probes to the homologs of the marker sequences disclosed herein can be employed preferably wherein non-human nucleic acid is being assayed.
5.4 Methods of Using Breast Cancer Marker Sets 5.4.1 Diagnostic MethodsThe present invention provides for methods of using the marker sets to analyze a sample from an individual so as to determine the individual's tumor type or subtype at a molecular level, whether a tumor is of the ER(+) or ER(−) type, and whether the tumor is BRCA1-associated or sporadic. The individual need not actually be afflicted with breast cancer. Essentially, the expression of specific marker genes in the individual, or a sample taken therefrom, is compared to a standard or control. For example, assume two breast cancer-related conditions, X and Y. One can compare the level of expression of breast cancer prognostic markers for condition X in an individual to the level of the marker-derived polynucleotides in a control, wherein the level represents the level of expression exhibited by samples having condition X. In this instance, if the expression of the markers in the individual's sample is substantially (i.e., statistically) different from that of the control, then the individual does not have condition X. Where, as here, the choice is bimodal (i.e., a sample is either X or Y), the individual can additionally be said to have condition Y. Of course, the comparison to a control representing condition Y can also be performed. Preferably both are performed simultaneously, such that each control acts as both a positive and a negative control. The distinguishing result may thus either be a demonstrable difference from the expression levels (i.e., the amount of marker-derived RNA, or polynucleotides derived therefrom) represented by the control, or no significant difference.
Thus, in one embodiment, the method of determining a particular tumor-related status of an individual comprises the steps of (1) hybridizing labeled target polynucleotides from an individual to a microarray containing one of the above marker sets; (2) hybridizing standard or control polynucleotides molecules to the microarray, wherein the standard or control molecules are differentially labeled from the target molecules; and (3) determining the difference in transcript levels, or lack thereof, between the target and standard or control, wherein the difference, or lack thereof, determines the individual's tumor-related status. In a more specific embodiment, the standard or control molecules comprise marker-derived polynucleotides from a pool of samples from normal individuals, or a pool of tumor samples from individuals having sporadic-type tumors. In a preferred embodiment, the standard or control is an artificially-generated pool of marker-derived polynucleotides, which pool is designed to mimic the level of marker expression exhibited by clinical samples of normal or breast cancer tumor tissue having a particular clinical indication (i.e., cancerous or non-cancerous; ER(+) or ER(−) tumor; BRCA1- or sporadic type tumor). In another specific embodiment, the control molecules comprise a pool derived from normal or breast cancer cell lines.
The present invention provides sets of markers useful for distinguishing ER(+) from ER(−) tumor types. Thus, in one embodiment of the above method, the level of polynucleotides (i.e., mRNA or polynucleotides derived therefrom) in a sample from an individual, expressed from the markers provided in Table 1 are compared to the level of expression of the same markers from a control, wherein the control comprises marker-related polynucleotides derived from ER(+) samples, ER(−) samples, or both. Preferably, the comparison is to both ER(+) and ER(−), and preferably the comparison is to polynucleotide pools from a number of ER(+) and ER(−) samples, respectively. Where the individual's marker expression most closely resembles or correlates with the ER(+) control, and does not resemble or correlate with the ER(−) control, the individual is classified as ER(+). Where the pool is not pure ER(+) or ER(−), for example, a sporadic pool is used. A set of experiments should be performed in which nucleic acids from individuals with known ER status are hybridized against the pool, in order to define the expression templates for the ER(+) and ER(−) group. Nucleic acids from each individual with unknown ER status are hybridized against the same pool and the expression profile is compared to the templates (s) to determine the individual's ER status.
The present invention provides sets of markers useful for distinguishing BRCA1-related tumors from sporadic tumors. Thus, the method can be performed substantially as for the ER(+/−) determination, with the exception that the markers are those listed in Tables 3 and 4, and the control markers are a pool of marker-derived polynucleotides BRCA1 tumor samples, and a pool of marker-derived polynucleotides from sporadic tumors. A patient is determined to have a BRCA1 germline mutation where the expression of the individual's marker-derived polynucleotides most closely resemble, or are most closely correlated with, that of the BRCA1 control. Where the control is not pure BRCA1 or sporadic, two templates can be defined in a manner similar to that for ER status, as described above.
For the above two embodiments of the method, the full set of markers may be used (i.e., the complete set of markers for Tables 1 or 3). In other embodiments, subsets of the markers may be used. In a preferred embodiment, the preferred markers listed in Tables 2 or 4 are used.
The similarity between the marker expression profile of an individual and that of a control can be assessed a number of ways. In the simplest case, the profiles can be compared visually in a printout of expression difference data. Alternatively, the similarity can be calculated mathematically.
In one embodiment, the similarity between two patients x and y, or patient x and a template y, expressed as a similarity value, can be calculated using the following equation:
In this equation, x and y are two patients with components of log ratio xi and yi, i=1 . . . , N=4,986. Associated with every value xi is error σx
is the error-weighted arithmetic mean.
In a preferred embodiment, templates are developed for sample comparison. The template is defined as the error-weighted log ratio average of the expression difference for the group of marker genes able to differentiate the particular breast cancer-related condition. For example, templates are defined for ER(+) samples and for ER(−) samples. Next, a classifier parameter is calculated. This parameter may be calculated using either expression level differences between the sample and template, or by calculation of a correlation coefficient. Such a coefficient, Pi, can be calculated using the following equation:
Pi=({right arrow over (z)}i·{right arrow over (y)})/(∥{right arrow over (z)}i∥·∥{right arrow over (y)}∥) Equation (1)
where zi is the expression template i, and y is the expression profile of a patient.
Thus, in a more specific embodiment, the above method of determining a particular tumor-related status of an individual comprises the steps of (1) hybridizing labeled target polynucleotides from an individual to a microarray containing one of the above marker sets; (2) hybridizing standard or control polynucleotides molecules to the microarray, wherein the standard or control molecules are differentially labeled from the target molecules; and (3) determining the ratio (or difference) of transcript levels between two channels (individual and control), or simply the transcript levels of the individual; and (4) comparing the results from (3) to the predefined templates, wherein said determining is accomplished by means of the statistic of Equation 1 or Equation 5, and wherein the difference, or lack thereof, determines the individual's tumor-related status.
5.4.2 Prognostic MethodsThe present invention provides sets of markers useful for classifying patients with into different prognostic categories. For example, the invention further provides a method for using these markers to determine whether an individual afflicted with breast cancer will have a good or poor clinical prognosis. The present invention further provides a method of further classifying “good prognosis” patients into two groups: those having a “very good prognosis” and those having an “intermediate prognosis.” For each of the above classifications, the invention further provides recommended therapeutic regimens.
The method can use the complete set of markers listed in Table 5. However, subsets of the markers listed in Table 5 may also be used. In a preferred embodiment, the subset of 70 markers listed in Table 6 is used. At least 5, 10, 15, 20, 25, 30, 40, 50, 60, or all 70 of the markers in Table 6 may be used.
Classification of a sample as “good prognosis” or “poor prognosis” is accomplished substantially as for the diagnostic markers described above, wherein a template is generated to which the marker expression levels in the sample are compared.
Thus, in one embodiment of the above method, the level of polynucleotides (i.e., mRNA or polynucleotides derived therefrom) in a sample from an individual breast cancer patient, expressed from the markers provided in Table 5, is compared to the level of expression of the same markers from a control, wherein the control comprises marker-related polynucleotides derived from breast cancer tumor samples taken from breast cancer patients clinically determined to have a good prognosis (“good prognosis” control), breast cancer patients clinically determined to have a poor prognosis (“poor prognosis” control), or both. The comparison may be to both good prognosis and poor prognosis controls, and preferably the comparison is to polynucleotide pools from a number of good prognosis and poor prognosis samples, respectively. Where the individual's marker expression most closely resembles or correlates with the good prognosis control, and does not resemble or correlate with the poor prognosis control, the individual is classified as having a good prognosis. Where the pool is not pure ‘good prognosis’ or ‘poor prognosis’, a set of experiments should be performed in which nucleic acids from samples from individuals with known outcomes are hybridized against the pool to define the expression templates for the good prognosis and poor prognosis groups. Nucleic acids from each individual with unknown outcome are hybridized against the same pool and the resulting expression profile is compared to the templates to predict its outcome.
The control or standard may be presented in a number of different formats. For example, the control, or template, to which the expression of marker genes in a breast cancer tumor sample is compared may be the average absolute level of expression of each of the genes in a pool of marker-derived nucleic acids pooled from breast cancer tumor samples obtained from a plurality of breast cancer patients. In this case, the difference between the absolute level of expression of these genes in the control and in a sample from a breast cancer patient provides the degree of similarity or dissimilarity of the level of expression in the patient sample and the control. The absolute level of expression may be measured by the intensity of the hybridization of the nucleic acids to an array. In other embodiments, the values for the expression levels of the markers in both the patient sample and control are transformed (see Section 5.4.3). For example, the expression level value for the patient, and the average expression level value for the pool, for each of the marker genes selected, may be transformed by taking the logarithm of the value. Moreover, the expression level values may be normalized by, for example, dividing by the median hybridization intensity of all of the samples that make up the pool. The control may be derived from hybridization data obtained simultaneously with the patient sample expression data, or may constitute a set of numerical values stores on a computer, or on computer-readable medium.
In one embodiment, the invention provides for method of determining whether an individual afflicted with breast cancer will likely experience a relapse within five years of initial diagnosis (i.e., whether an individual has a poor prognosis) comprising (1) comparing the level of expression of the markers listed in Table 5 in a sample taken from the individual to the level of the same markers in a standard or control, where the standard or control levels represent those found in an individual with a poor prognosis; and (2) determining whether the level of the marker-related polynucleotides in the sample from the individual is significantly different than that of the control, wherein if no substantial difference is found, the patient has a poor prognosis, and if a substantial difference is found, the patient has a good prognosis. Persons of skill in the art will readily see that the markers associated with good prognosis can also be used as controls. In a more specific embodiment, both controls are run.
Poor prognosis of breast cancer may indicate that a tumor is relatively aggressive, while good prognosis may indicate that a tumor is relatively nonaggressive. Therefore, the invention provides for a method of determining a course of treatment of a breast cancer patient, comprising determining whether the level of expression of the 231 markers of Table 5, or a subset thereof, correlates with the level of these markers in a sample representing a good prognosis expression pattern or a poor prognosis pattern; and determining a course of treatment, wherein if the expression correlates with the poor prognosis pattern, the tumor is treated as an aggressive tumor.
Patients having an expression profile correlating with the good prognosis profile may be further divided into “very good prognosis” and “intermediate prognosis” groups. In the original 78 samples used to determine the 70 optimal prognostic marker genes, patients whose expression profile correlated with (i.e., had a correlation coefficient less than 0.40) the average “good prognosis” expression profile were classified as having a “good prognosis.” It was subsequently found that tumors with an expression profile having a coefficient of correlation to the average “good prognosis” expression profile greater than 0.636 developed no distant metastases. These patients may receive a different therapeutic regimen than patients whose tumors have a “good prognosis” expression profile that correlates less strongly to the average “good prognosis” expression profile. Accordingly, patients were classified as having a “very good prognosis” expression profile if the correlation coefficient exceeded 0.636, and an “intermediate prognosis” if their expression profile correlation coefficient was 0.39 or less but less than or equal to 0.636. The data for the 70 genes listed in Table 6 for these 78 patients is listed in Table 7.
This methodology may be generalized to situations in which data from other groups of patients is used, where a group of patients is to provide clinical and expression data to be used for classification of subsequent breast cancer patients. A group of patients is selected for which clinical and followup data are available for at least five years after initial diagnosis. Preferably the patients in the group are selected as a consecutive series to reduce or eliminate selection bias. Breast cancer tumor samples are taken from each patient, and marker-related polynucleotides are generated. The expression levels of each of the marker genes listed in Table 5 or a subset thereof, preferably at least five of the marker genes listed in Table 6, is determined for each tumor sample (i.e., for each patient) to generate a patient expression profile. Marker-derived polynucleotides from patients within the group clinically determined to have a good prognosis (i.e., no distant metastases within five years of initial diagnosis) are pooled and mean expression levels for each of the prognosis-related marker genes are determined to obtain a control expression profile. Patients are then rank ordered in descending order of similarity of patient expression profiles to the control expression profile to produce a rank-ordered list of patients, where the similarity is a value expressed by a single similarity metric such as a correlation coefficient. A first threshold similarity value is then selected, which divides the group of patients into those predicted to have a good prognosis and those predicted to have a poor prognosis. This first threshold similarity value may be the similarity value that most accurately predicts clinical outcomes (i.e., results in an expression profile classification that results in the fewest misclassifications when compared to actual clinical outcomes), or a similarity value that results in a particular number or percentage of false negatives in the group, where a false negative is an expression-based good prognosis prediction for a breast cancer patient that actually develops a distant metastasis within the five year period after initial diagnosis. A second threshold similarity value is then selected which divides the “good prognosis” group into two groups. This threshold similarity value is determined empirically as the similarity value for the patient highest on the rank-ordered list of patients who actually develops a distant metastasis within the five-year period. This second threshold similarity value divides the “good prognosis” group into a group of patients having a “very good prognosis,” i.e., those having similarity values equal to or higher than the second threshold similarity value, and an “intermediate prognosis” group, i.e., those having a similarity value equal to or greater than the first threshold similarity value, but less than the second threshold similarity value. Patients whose similarity values are less than the first threshold similarity value are classified as having a “poor prognosis.” Subsequent patients may be similarly classified by calculating a similarity value for the patient, where the control is the “good prognosis” template or expression profile, and comparison of this similarity metric to the similarity metrics obtained above.
Thus, in one embodiment, the invention provides a method for classifying a breast cancer patient according to prognosis, comprising comparing the levels of expression of at least five of the genes for which markers are listed in Table 5 in a cell sample taken from said breast cancer patient to control levels of expression of said at least five genes; and classifying said breast cancer patient according to prognosis of his or her breast cancer based on the similarity between said levels of expression in said cell sample and said control levels. In a more specific embodiment, the second step of this method comprises determining whether said similarity exceeds one or more predetermined threshold values of similarity. In another more specific embodiment of this method, said control levels are the mean levels of expression of each of said at least five genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have no distant metastases within five years of initial diagnosis. In another more specific embodiment of this method, said control levels comprise the expression levels of said genes in breast cancer patients who have had no distant metastases within five years of initial diagnosis. In yet another more specific embodiment of this method, said control levels comprise, for each of said at least five of the genes for which markers are listed in Table 5, mean log intensity values stored on a computer. In yet another more specific embodiment of this method, said control levels comprise, for each of said at least five of the genes for which markers are listed in Table 6, mean log intensity values stored on a computer. In another more specific embodiment of this method, said control levels comprise, for each of said at least five genes listed in Table 6, the mean log intensity values that are listed in Table 7. The set of mean log intensity values listed in this table may be used as a “good prognosis” template for any of the prognostic methods described herein. The above method may also compare the level of expression of at least ten, 20, 30, 40, 50, 75, 100 or more genes for which markers listed in Table 5, or may use the 70 preferred genes for which markers are listed in Table 6.
The present invention also provides for the classification of a breast cancer patient into one of three prognostic categories comprising (a) determining the similarity between the level of expression of at least five of the genes for which markers are listed in Table 5 to control levels of expression to obtain a patient similarity value; (b) providing a first threshold similarity value that differentiates persons having a good prognosis from those having a poor prognosis, and providing determining a second threshold similarity value, where said second threshold similarity value indicates a higher degree of similarity of the expression of said genes to said control than said first similarity value; and (c) classifying the breast cancer patient into a first prognostic category if the patient similarity value exceeds the first and second threshold similarity values, a second prognostic category if the patient similarity value equals or exceeds the first but not the second threshold similarity value, and a third prognostic category if the patient similarity value is less than the first threshold similarity value. In a more specific embodiment, the levels of expression of each of said at least five genes is determined first. As above, the control comprises marker-related polynucleotides derived from breast cancer tumor samples taken from breast cancer patients clinically determined to have a good prognosis (“good prognosis” control), breast cancer patients clinically determined to have a poor prognosis “poor prognosis” control), or both. In a preferred embodiment, the control is a “good prognosis” control or template, i.e., a control or template comprising the mean levels of expression of said genes in breast cancer patients who have had no distant metastases within five years of initial diagnosis. In another more specific embodiment, said control levels comprise a set of values, for example mean log intensity values, preferably normalized, stored on a computer. In a more specific embodiment, said control or template is the set of mean log intensity values shown in Table 7. In another specific embodiment, said determining in step (a) may be accomplished by a method comprising determining the difference between the absolute expression level of each of said genes and the average expression level of the same genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis. In another specific embodiment, said determining in step (a) may be accomplished by a method comprising determining the degree of similarity between the level of expression of each of said genes in a breast cancer tumor sample taken from a breast cancer patient and the level of expression of the same genes in a pool of tumor samples obtained from a plurality of breast cancer patients who have had no relapse of breast cancer within five years of initial diagnosis.
In a specific embodiment of the above method, said first threshold similarity value and said second threshold similarity values are selected by a method comprising (a) rank ordering in descending order said tumor samples that compose said pool of tumor samples by the degree of similarity between the level of expression of said genes in each of said tumor samples to the mean level of expression of the same genes of the remaining tumor samples that compose said pool to obtain a rank-ordered list, said degree of similarity being expressed as a similarity value; (b) determining an acceptable number of false negatives in said classifying, wherein said false negatives are breast cancer patients for whom the expression levels of said at least five of the genes for which markers are listed in Table 5 in said cell sample predicts that said patient will have no distant metastases within the first five years after initial diagnosis, but who has had a distant metastasis within the first five years after initial diagnosis; (c) determining a similarity value above which in said rank ordered list fewer than said acceptable number of tumor samples are false negatives; and (d) selecting said similarity value determined in step (c) as said first threshold similarity value; and (e) selecting a second similarity value, greater than said first similarity value, as said second threshold similarity value. In an even more specific embodiment of this method, said second threshold similarity value is selected in step (e) by a method comprising determining which of said tumor samples, taken from patients having a distant metastasis within five years of initial diagnosis, in said rank ordered list has the greatest similarity value, and selecting said greatest similarity value as said second threshold similarity value. In even more specific embodiments, said first and second threshold similarity values are correlation coefficients, and said first threshold similarity value is 0.4 and said second threshold similarity value is greater than 0.4. In another even more specific embodiment, using the template data provided in Table 7, said first and second threshold similarity values are correlation coefficients, and said second threshold similarity value is 0.636. In another specific embodiment, said first similarity value is a similarity value above which at most 10% false negatives are predicted in a training set of tumors, and said second correlation coefficient is a coefficient above which at most 5% false negatives are predicted in said training set of tumors. In another specific embodiment, said first correlation coefficient is a coefficient above which 10% false negatives are predicted in a training set of tumors, and said second correlation coefficient is a coefficient above which no false negatives are predicted in said training set of tumors. In the above and other embodiments, “false negatives” are patients classified by the expression of the marker genes as having a good prognosis, or who are predicted by such expression to have a good prognosis, but who actually do develop distant metastases within five years.
In a specific embodiment of the above methods, the first, second and third prognostic categories are “very good prognosis,” “intermediate prognosis,” and “poor prognosis,” respectively. Patients classified into the first prognostic category (“very good prognosis”) are likely not to have a distant metastasis within five years of initial diagnosis. Patients classified as having an “intermediate prognosis” are also unlikely to have a distant metastasis within five years of initial diagnosis, but may be recommended to undergo a different therapeutic regimen than patients having a “very good prognosis” marker gene expression profile (see below). Patients classified into the third prognostic category (“poor prognosis”) are likely to have a distant metastasis within five years of initial diagnosis.
In a more specific embodiment, the similarity value is the degree of difference between the absolute (i.e., untransformed) level of expression of each of the genes in a tumor sample taken from a breast cancer patient and the mean absolute level of expression of the same genes in a control. In another more specific embodiment, the similarity value is calculated using expression level data that is transformed (see Section 5.4.3). In another more specific embodiment, the similarity value is expressed as a similarity metric, such as a correlation coefficient, representing the similarity between the level of expression of the marker genes in the tumor sample and the mean level of expression of the same genes in a plurality of breast cancer tumor samples taken from breast cancer patients.
In another specific embodiment, said first and second similarity values are derived from control expression data obtained in the same hybridization experiment as that in which the patient expression level data is obtained. In another specific embodiment, said first and second similarity values are derived from an existing set of expression data. In a more specific embodiment, said first and second correlation coefficients are derived from a mathematical sample pool (see Section 5.4.3; Example 9). For example, comparison of the expression of marker genes in new tumor samples may be compared to the pre-existing template determined for these genes for the 78 patients in the initial study; the template, or average expression levels of each of the seventy genes can be used as a reference or control for any tumor sample. Preferably, the comparison is made to a template comprising the average expression level of at least five of the 70 genes listed in Table 6 for the 44 out of 78 patients clinically determined to have a good prognosis. The coefficient of correlation of the level of expression of these genes in the tumor sample to the 44 “good prognosis” patient template is then determined to produce a tumor correlation coefficient. For this control patient set, two similarity values have been derived: a first correlation coefficient of 0.4 and a second correlation coefficient of 0.636, derived using the 70 marker gene set listed in Table 6. New breast cancer patients whose coefficients of correlation of the expression of these marker genes with the 44-patient “good prognosis” template equal or exceed 0.636 are classified as having a “very good prognosis”; those having a coefficient of correlation of between 0.4 and 0.635 are classified as having an “intermediate prognosis”; and those having a correlation coefficient of 0.39 or less are classified as having a “poor prognosis.”
Because the above methods may utilize arrays to which fluorescently-labeled marker-derived target nucleic acids are hybridized, the invention also provides a method of classifying a breast cancer patient according to prognosis comprising the steps of (a) contacting first nucleic acids derived from a tumor sample taken from said breast cancer patient, and second nucleic acids derived from two or more tumor samples from breast cancer patients who have had no distant metastases within five years of initial diagnosis, with an array under conditions such that hybridization can occur, detecting at each of a plurality of discrete loci on said array a first fluorescent emission signal from said first nucleic acids and a second fluorescent emission signal from said second nucleic acids that are bound to said array under said conditions, wherein said array comprises at least five of the genes for which markers are listed in Table 5 and wherein at least 50% of the probes on said array are listed in Table 5; (b) calculating the similarity between said first fluorescent emission signals and said second fluorescent emission signals across said at least five genes; and (c) classifying said breast cancer patient according to prognosis of his or her breast cancer based on the similarity between said first fluorescent emission signals and said second fluorescent emission signals across said at least five genes.
Once patients have been classified as having a “very good prognosis,” “intermediate prognosis” or “poor prognosis,” this information can be combined with the patient's clinical data to determine an appropriate treatment regimen. In one embodiment, the patient's lymph node metastasis status (i.e., whether the patient is pN+ or pN0) is determined. Patients who are pN0 and have a “very good prognosis” or “intermediate” expression profile may be treated without adjuvant chemotherapy. All other patients should be treated with adjuvant chemotherapy. In a more specific embodiment, the patient's estrogen receptor status is also identified (i.e., whether the patient is ER(+) or ER(−)). Here, patients classified as having an “intermediate prognosis” or “poor prognosis” who are ER(+) are assigned a therapeutic regimen that additionally comprises adjuvant hormonal therapy.
Thus, the invention provides for a method of assigning a therapeutic regimen to a breast cancer patient, comprising (a) classifying said patient as having a “poor prognosis,” “intermediate prognosis,” or “very good prognosis” on the basis of the levels of expression of at least five of the genes for which markers are listed in Table 5; and (b) assigning said patient a therapeutic regimen, said therapeutic regimen comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or comprising chemotherapy if said patient has any other combination of lymph node status and expression profile. In another embodiment, the invention provides a method for assigning a therapeutic regimen for a breast cancer patient, comprising determining the lymph node status for said patient; determining the level of expression of at least five of the genes listed in Table 5 in a tumor sample from said patient, thereby generating an expression profile; classifying said patient as having a “poor prognosis”, “intermediate prognosis” or “very good prognosis” on the basis of said expression profile; and assigning the patient a therapeutic regimen, said therapeutic regimen comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or a therapeutic regiment comprising chemotherapy if said patient has any other combination of lymph node status and expression profile. In a more specific embodiment of the above methods, the ER status of the patient is additionally determined, and if the breast cancer patient is ER(+) and has an intermediate or poor prognosis, the therapeutic regimen additionally comprises hormonal therapy. Because in the training set of 78 breast cancer patients it was determined that the great majority of intermediate prognosis patients were also ER(+) (see Example 10), another more specific embodiment is to determine the lymph node status and expression profiles, and to assign intermediate prognosis patients adjuvant hormonal therapy (whether or not ER status has been determined). In another specific embodiment, the breast cancer patient is 52 years of age or younger. In another specific embodiment, the breast cancer patient is premenopausal. In another specific embodiment, the breast cancer patient has stage I or stage II breast cancer.
The use of marker sets is not restricted to the prognosis of breast cancer-related conditions, and may be applied in a variety of phenotypes or conditions, clinical or experimental, in which gene expression plays a role. Where a set of markers has been identified that corresponds to two or more phenotypes, the marker set can be used to distinguish these phenotypes. For example, the phenotypes may be the diagnosis and/or prognosis of clinical states or phenotypes associated with other cancers, other disease conditions, or other physiological conditions, wherein the expression level data is derived from a set of genes correlated with the particular physiological or disease condition. Further, the expression of markers specific to other types of cancer may be used to differentiate patients or patient populations for those cancers for which different therapeutic regimens are indicated.
5.4.3 Improving Sensitivity to Expression Level DifferencesIn using the markers disclosed herein, and, indeed, using any sets of markers to differentiate an individual having one phenotype from another individual having a second phenotype, one can compare the absolute expression of each of the markers in a sample to a control; for example, the control can be the average level of expression of each of the markers, respectively, in a pool of individuals. To increase the sensitivity of the comparison, however, the expression level values are preferably transformed in a number of ways.
For example, the expression level of each of the markers can be normalized by the average expression level of all markers the expression level of which is determined, or by the average expression level of a set of control genes. Thus, in one embodiment, the markers are represented by probes on a microarray, and the expression level of each of the markers is normalized by the mean or median expression level across all of the genes represented on the microarray, including any non-marker genes. In a specific embodiment, the normalization is carried out by dividing the median or mean level of expression of all of the genes on the microarray. In another embodiment, the expression levels of the markers is normalized by the mean or median level of expression of a set of control markers. In a specific embodiment, the control markers comprise a set of housekeeping genes. In another specific embodiment, the normalization is accomplished by dividing by the median or mean expression level of the control genes.
The sensitivity of a marker-based assay will also be increased if the expression levels of individual markers are compared to the expression of the same markers in a pool of samples. Preferably, the comparison is to the mean or median expression level of each the marker genes in the pool of samples. Such a comparison may be accomplished, for example, by dividing by the mean or median expression level of the pool for each of the markers from the expression level each of the markers in the sample. This has the effect of accentuating the relative differences in expression between markers in the sample and markers in the pool as a whole, making comparisons more sensitive and more likely to produce meaningful results that the use of absolute expression levels alone. The expression level data may be transformed in any convenient way; preferably, the expression level data for all is log transformed before means or medians are taken.
In performing comparisons to a pool, two approaches may be used. First, the expression levels of the markers in the sample may be compared to the expression level of those markers in the pool, where nucleic acid derived from the sample and nucleic acid derived from the pool are hybridized during the course of a single experiment. Such an approach requires that new pool nucleic acid be generated for each comparison or limited numbers of comparisons, and is therefore limited by the amount of nucleic acid available. Alternatively, and preferably, the expression levels in a pool, whether normalized and/or transformed or not, are stored on a computer, or on computer-readable media, to be used in comparisons to the individual expression level data from the sample (i.e., single-channel data).
Thus, the current invention provides the following method of classifying a first cell or organism as having one of at least two different phenotypes, where the different phenotypes comprise a first phenotype and a second phenotype. The level of expression of each of a plurality of genes in a first sample from the first cell or organism is compared to the level of expression of each of said genes, respectively, in a pooled sample from a plurality of cells or organisms, the plurality of cells or organisms comprising different cells or organisms exhibiting said at least two different phenotypes, respectively, to produce a first compared value. The first compared value is then compared to a second compared value, wherein said second compared value is the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having said first phenotype to the level of expression of each of said genes, respectively, in the pooled sample. The first compared value is then compared to a third compared value, wherein said third compared value is the product of a method comprising comparing the level of expression of each of the genes in a sample from a cell or organism characterized as having the second phenotype to the level of expression of each of the genes, respectively, in the pooled sample. Optionally, the first compared value can be compared to additional compared values, respectively, where each additional compared value is the product of a method comprising comparing the level of expression of each of said genes in a sample from a cell or organism characterized as having a phenotype different from said first and second phenotypes but included among the at least two different phenotypes, to the level of expression of each of said genes, respectively, in said pooled sample. Finally, a determination is made as to which of said second, third, and, if present, one or more additional compared values, said first compared value is most similar, wherein the first cell or organism is determined to have the phenotype of the cell or organism used to produce said compared value most similar to said first compared value.
In a specific embodiment of this method, the compared values are each ratios of the levels of expression of each of said genes. In another specific embodiment, each of the levels of expression of each of the genes in the pooled sample are normalized prior to any of the comparing steps. In a more specific embodiment, the normalization of the levels of expression is carried out by dividing by the median or mean level of the expression of each of the genes or dividing by the mean or median level of expression of one or more housekeeping genes in the pooled sample from said cell or organism. In another specific embodiment, the normalized levels of expression are subjected to a log transform, and the comparing steps comprise subtracting the log transform from the log of the levels of expression of each of the genes in the sample. In another specific embodiment, the two or more different phenotypes are different stages of a disease or disorder. In still another specific embodiment, the two or more different phenotypes are different prognoses of a disease or disorder. In yet another specific embodiment, the levels of expression of each of the genes, respectively, in the pooled sample or said levels of expression of each of said genes in a sample from the cell or organism characterized as having the first phenotype, second phenotype, or said phenotype different from said first and second phenotypes, respectively, are stored on a computer or on a computer-readable medium.
In another specific embodiment, the two phenotypes are ER(+) or ER(−) status. In another specific embodiment, the two phenotypes are BRCA1 or sporadic tumor-type status. In yet another specific embodiment, the two phenotypes are good prognosis and poor prognosis.
In another specific embodiment, the comparison is made between the expression of each of the genes in the sample and the expression of the same genes in a pool representing only one of two or more phenotypes. In the context of prognosis-correlated genes, for example, one can compare the expression levels of prognosis-related genes in a sample to the average level of the expression of the same genes in a “good prognosis” pool of samples (as opposed to a pool of samples that include samples from patients having poor prognoses and good prognoses). Thus, in this method, a sample is classified as having a good prognosis if the level of expression of prognosis-correlated genes exceeds a chosen coefficient of correlation to the average “good prognosis” expression profile (i.e., the level of expression of prognosis-correlated genes in a pool of samples from patients having a “good prognosis.” Patients whose expression levels correlate more poorly with the “good prognosis” expression profile (i.e., whose correlation coefficient fails to exceed the chosen coefficient) are classified as having a poor prognosis. The method can be applied to subdivisions of these prognostic classes. For example, in a specific embodiment, the phenotype is good prognosis and said determination comprises (1) determining the coefficient of correlation between the expression of said plurality of genes in the sample and of the same genes in said pooled sample; (2) selecting a first correlation coefficient value between 0.4 and +1 and a second correlation coefficient value between 0.4 and +1, wherein said second value is larger than said first value; and (3) classifying said sample as “very good prognosis” if said coefficient of correlation equals or is greater than said second correlation coefficient value, “intermediate prognosis” if said coefficient of correlation equals or exceeds said first correlation coefficient value, and is less than said second correlation coefficient value, or “poor prognosis” if said coefficient of correlation is less than said first correlation coefficient value.
Of course, single-channel data may also be used without specific comparison to a mathematical sample pool. For example, a sample may be classified as having a first or a second phenotype, wherein the first and second phenotypes are related, by calculating the similarity between the expression of at least 5 markers in the sample, where the markers are correlated with the first or second phenotype, to the expression of the same markers in a first phenotype template and a second phenotype template, by (a) labeling nucleic acids derived from a sample with a fluorophore to obtain a pool of fluorophore-labeled nucleic acids; (b) contacting said fluorophore-labeled nucleic acid with a microarray under conditions such that hybridization can occur, detecting at each of a plurality of discrete loci on the microarray a flourescent emission signal from said fluorophore-labeled nucleic acid that is bound to said microarray under said conditions; and (c) determining the similarity of marker gene expression in the individual sample to the first and second templates, wherein if said expression is more similar to the first template, the sample is classified as having the first phenotype, and if said expression is more similar to the second template, the sample is classified as having the second phenotype.
5.5 Determination of Marker Gene Expression Levels 5.5.1 MethodsThe expression levels of the marker genes in a sample may be determined by any means known in the art. The expression level may be determined by isolating and determining the level (i.e., amount) of nucleic acid transcribed from each marker gene. Alternatively, or additionally, the level of specific proteins translated from mRNA transcribed from a marker gene may be determined.
The level of expression of specific marker genes can be accomplished by determining the amount of mRNA, or polynucleotides derived therefrom, present in a sample. Any method for determining RNA levels can be used. For example, RNA is isolated from a sample and separated on an agarose gel. The separated RNA is then transferred to a solid support, such as a filter. Nucleic acid probes representing one or more markers are then hybridized to the filter by northern hybridization, and the amount of marker-derived RNA is determined. Such determination can be visual, or machine-aided, for example, by use of a densitometer. Another method of determining RNA levels is by use of a dot-blot or a slot-blot. In this method, RNA, or nucleic acid derived therefrom, from a sample is labeled. The RNA or nucleic acid derived therefrom is then hybridized to a filter containing oligonucleotides derived from one or more marker genes, wherein the oligonucleotides are placed upon the filter at discrete, easily-identifiable locations. Hybridization, or lack thereof, of the labeled RNA to the filter-bound oligonucleotides is determined visually or by densitometer. Polynucleotides can be labeled using a radiolabel or a fluorescent (i.e., visible) label.
These examples are not intended to be limiting; other methods of determining RNA abundance are known in the art.
The level of expression of particular marker genes may also be assessed by determining the level of the specific protein expressed from the marker genes. This can be accomplished, for example, by separation of proteins from a sample on a polyacrylamide gel, followed by identification of specific marker-derived proteins using antibodies in a western blot. Alternatively, proteins can be separated by two-dimensional gel electrophoresis systems. Two-dimensional gel electrophoresis is well-known in the art and typically involves isoelectric focusing along a first dimension followed by SDS-PAGE electrophoresis along a second dimension. See, e.g., Hames et al, 1990, G
Alternatively, marker-derived protein levels can be determined by constructing an antibody microarray in which binding sites comprise immobilized, preferably monoclonal, antibodies specific to a plurality of protein species encoded by the cell genome. Preferably, antibodies are present for a substantial fraction of the marker-derived proteins of interest. Methods for making monoclonal antibodies are well known (see, e.g., Harlow and Lane, 1988, A
Finally, expression of marker genes in a number of tissue specimens may be characterized using a “tissue array” (Kononen et al., Nat. Med 4(7):844–7 (1998)). In a tissue array, multiple tissue samples are assessed on the same microarray. The arrays allow in situ detection of RNA and protein levels; consecutive sections allow the analysis of multiple samples simultaneously.
5.5.2 MicroarraysIn preferred embodiments, polynucleotide microarrays are used to measure expression so that the expression status of each of the markers above is assessed simultaneously. In a specific embodiment, the invention provides for oligonucleotide or cDNA arrays comprising probes hybridizable to the genes corresponding to each of the marker sets described above (i.e., markers to determine the molecular type or subtype of a tumor; markers to distinguish ER status; markers to distinguish BRCA1 from sporadic tumors; markers to distinguish patients with good versus patients with poor prognosis; markers to distinguish both ER(+) from ER(−), and BRCA1 tumors from sporadic tumors; markers to distinguish ER(+) from ER(−), and patients with good prognosis from patients with poor prognosis; markers to distinguish BRCA1 tumors from sporadic tumors, and patients with good prognosis from patients with poor prognosis; and markers able to distinguish ER(+) from ER(−), BRCA1 tumors from sporadic tumors, and patients with good prognosis from patients with poor prognosis; and markers unique to each status).
The microarrays provided by the present invention may comprise probes hybridizable to the genes corresponding to markers able to distinguish the status of one, two, or all three of the clinical conditions noted above. In particular, the invention provides polynucleotide arrays comprising probes to a subset or subsets of at least 50, 100, 200, 300, 400, 500, 750, 1,000, 1,250, 1,500, 1,750, 2,000 or 2,250 genetic markers, up to the full set of 2,460 markers, which distinguish ER(+) and ER(−) patients or tumors. The invention also provides probes to subsets of at least 20, 30, 40, 50, 75, 100, 150, 200, 250, 300, 350 or 400 markers, up to the full set of 430 markers, which distinguish between tumors containing a BRCA1 mutation and sporadic tumors within an ER(−) group of tumors. The invention also provides probes to subsets of at least 20, 30, 40, 50, 75, 100, 150 or 200 markers, up to the full set of 231 markers, which distinguish between patients with good and poor prognosis within sporadic tumors. In a specific embodiment, the array comprises probes to marker sets or subsets directed to any two of the clinical conditions. In a more specific embodiment, the array comprises probes to marker sets or subsets directed to all three clinical conditions.
In specific embodiments, the invention provides polynucleotide arrays in which the breast cancer-related markers described herein comprise at least 50%, 60%, 70%, 80%, 85%, 90%, 95% or 98% of the probes on said array. In another specific embodiment, the invention provides polynucleotide arrays in which ER status-related markers selected from Table 1 comprise at least 50%, 60%, 70%, 80%, 85%, 90%, 95% or 98% of the probes on said array. In another specific embodiment, the invention provides polynucleotide arrays in which BRCA1/sporadic markers selected from Table 3 comprise at least 50%, 60%, 70%, 80%, 85%, 90%, 95% or 98% of the probes on said array. In another specific embodiment, the invention provides polynucleotide arrays in which prognostic markers selected from Table 5 comprise at least 50%, 60%, 70%, 80%, 85%, 90%, 95% or 98% of the probes on said array.
In yet another specific embodiment, microarrays that are used in the methods disclosed herein optionally comprise markers additional to at least some of the markers listed in Tables 1–6. For example, in a specific embodiment, the microarray is a screening or scanning array as described in Altschuler et al., International Publication WO 02/18646, published Mar. 7, 2002 and Scherer et al., International Publication WO 02/16650, published Feb. 28, 2002. The scanning and screening arrays comprise regularly-spaced, positionally-addressable probes derived from genomic nucleic acid sequence, both expressed and unexpressed. Such arrays may comprise probes corresponding to a subset of, or all of, the markers listed in Tables 1–6, or a subset thereof as described above, and can be used to monitor marker expression in the same way as a microarray containing only markers listed in Tables 1–6.
In yet another specific embodiment, the microarray is a commercially-available cDNA microarray that comprises at least five of the markers listed in Tables 1–6. Preferably, a commercially-available cDNA microarray comprises all of the markers listed in Tables 1–6. However, such a microarray may comprise 5, 10, 15, 25, 50, 100, 150, 250, 500, 1000 or more of the markers in any of Tables 1–6, up to the maximum number of markers in a Table, and may comprise all of the markers in any one of Tables 1–6 and a subset of another of Tables 1–6, or subsets of each as described above. In a specific embodiment of the microarrays used in the methods disclosed herein, the markers that are all or a portion of Tables 1–6 make up at least 50%, 60%, 70%, 80%, 90%, 95% or 98% of the probes on the microarray.
General methods pertaining to the construction of microarrays comprising the marker sets and/or subsets above are described in the following sections.
5.5.2.1 Construction of MicroarraysMicroarrays are prepared by selecting probes which comprise a polynucleotide sequence, and then immobilizing such probes to a solid support or surface. For example, the probes may comprise DNA sequences, RNA sequences, or copolymer sequences of DNA and RNA. The polynucleotide sequences of the probes may also comprise DNA and/or RNA analogues, or combinations thereof. For example, the polynucleotide sequences of the probes may be full or partial fragments of genomic DNA. The polynucleotide sequences of the probes may also be synthesized nucleotide sequences, such as synthetic oligonucleotide sequences. The probe sequences can be synthesized either enzymatically in vivo, enzymatically in vitro (e.g., by PCR), or non-enzymatically in vitro.
The probe or probes used in the methods of the invention are preferably immobilized to a solid support which may be either porous or non-porous. For example, the probes of the invention may be polynucleotide sequences which are attached to a nitrocellulose or nylon membrane or filter covalently at either the 3′ or the 5′ end of the polynucleotide. Such hybridization probes are well known in the art (see, e.g., Sambrook et al., M
In preferred embodiments, a microarray comprises a support or surface with an ordered array of binding (e.g., hybridization) sites or “probes” each representing one of the markers described herein. Preferably the microarrays are addressable arrays, and more preferably positionally addressable arrays. More specifically, each probe of the array is preferably located at a known, predetermined position on the solid support such that the identity (i.e., the sequence) of each probe can be determined from its position in the array (i.e., on the support or surface). In preferred embodiments, each probe is covalently attached to the solid support at a single site.
Microarrays can be made in a number of ways, of which several are described below. However produced, microarrays share certain characteristics. The arrays are reproducible, allowing multiple copies of a given array to be produced and easily compared with each other. Preferably, microarrays are made from materials that are stable under binding (e.g., nucleic acid hybridization) conditions. The microarrays are preferably small, e.g., between 1 cm2 and 25 cm2, between 12 cm2 and 13 cm2, or 3 cm2. However, larger arrays are also contemplated and may be preferable, e.g., for use in screening arrays. Preferably, a given binding site or unique set of binding sites in the microarray will specifically bind (e.g., hybridize) to the product of a single gene in a cell (e.g., to a specific mRNA, or to a specific cDNA derived therefrom). However, in general, other related or similar sequences will cross hybridize to a given binding site.
The microarrays of the present invention include one or more test probes, each of which has a polynucleotide sequence that is complementary to a subsequence of RNA or DNA to be detected. Preferably, the position of each probe on the solid surface is known. Indeed, the microarrays are preferably positionally addressable arrays. Specifically, each probe of the array is preferably located at a known, predetermined position on the solid support such that the identity (i.e., the sequence) of each probe can be determined from its position on the array (i.e., on the support or surface).
According to the invention, the microarray is an array (i.e., a matrix) in which each position represents one of the markers described herein. For example, each position can contain a DNA or DNA analogue based on genomic DNA to which a particular RNA or cDNA transcribed from that genetic marker can specifically hybridize. The DNA or DNA analogue can be, e.g., a synthetic oligomer or a gene fragment. In one embodiment, probes representing each of the markers is present on the array. In a preferred embodiment, the array comprises the 550 of the 2,460 RE-status markers, 70 of the BRCA1/sporadic markers, and all 231 of the prognosis markers.
5.5.2.2 Preparing Probes for MicroarraysAs noted above, the “probe” to which a particular polynucleotide molecule specifically hybridizes according to the invention contains a complementary genomic polynucleotide sequence. The probes of the microarray preferably consist of nucleotide sequences of no more than 1,000 nucleotides. In some embodiments, the probes of the array consist of nucleotide sequences of 10 to 1,000 nucleotides. In a preferred embodiment, the nucleotide sequences of the probes are in the range of 10–200 nucleotides in length and are genomic sequences of a species of organism, such that a plurality of different probes is present, with sequences complementary and thus capable of hybridizing to the genome of such a species of organism, sequentially tiled across all or a portion of such genome. In other specific embodiments, the probes are in the range of 10–30 nucleotides in length, in the range of 10–40 nucleotides in length, in the range of 20–50 nucleotides in length, in the range of 40–80 nucleotides in length, in the range of 50–150 nucleotides in length, in the range of 80–120 nucleotides in length, and most preferably are 60 nucleotides in length.
The probes may comprise DNA or DNA “mimics” (e.g., derivatives and analogues) corresponding to a portion of an organism's genome. In another embodiment, the probes of the microarray are complementary RNA or RNA mimics. DNA mimics are polymers composed of subunits capable of specific, Watson-Crick-like hybridization with DNA, or of specific hybridization with RNA. The nucleic acids can be modified at the base moiety, at the sugar moiety, or at the phosphate backbone. Exemplary DNA mimics include, e.g., phosphorothioates.
DNA can be obtained, e.g., by polymerase chain reaction (PCR) amplification of genomic DNA or cloned sequences. PCR primers are preferably chosen based on a known sequence of the genome that will result in amplification of specific fragments of genomic DNA. Computer programs that are well known in the art are useful in the design of primers with the required specificity and optimal amplification properties, such as Oligo version 5.0 (National Biosciences). Typically each probe on the microarray will be between 10 bases and 50,000 bases, usually between 300 bases and 1,000 bases in length. PCR methods are well known in the art, and are described, for example, in Innis et al., eds., PCR P
An alternative, preferred means for generating the polynucleotide probes of the microarray is by synthesis of synthetic polynucleotides or oligonucleotides, e.g., using N-phosphonate or phosphoramidite chemistries (Froehler et al., Nucleic Acid Res. 14:5399–5407 (1986); McBride et al., Tetrahedron Lett. 24:246–248 (1983)). Synthetic sequences are typically between about 10 and about 500 bases in length, more typically between about 20 and about 100 bases, and most preferably between about 40 and about 70 bases in length. In some embodiments, synthetic nucleic acids include non-natural bases, such as, but by no means limited to, inosine. As noted above, nucleic acid analogues may be used as binding sites for hybridization. An example of a suitable nucleic acid analogue is peptide nucleic acid (see, e.g., Egholm et al., Nature 363:566–568 (1993); U.S. Pat. No. 5,539,083). Probes are preferably selected using an algorithm that takes into account binding energies, base composition, sequence complexity, cross-hybridization binding energies, and secondary structure (see Friend et al., International Patent Publication WO 01/05935, published Jan. 25, 2001; Hughes et al., Nat. Biotech. 19:342–7 (2001)).
A skilled artisan will also appreciate that positive control probes, e.g., probes known to be complementary and hybridizable to sequences in the target polynucleotide molecules, and negative control probes, e.g., probes known to not be complementary and hybridizable to sequences in the target polynucleotide molecules, should be included on the array. In one embodiment, positive controls are synthesized along the perimeter of the array. In another embodiment, positive controls are synthesized in diagonal stripes across the array. In still another embodiment, the reverse complement for each probe is synthesized next to the position of the probe to serve as a negative control. In yet another embodiment, sequences from other species of organism are used as negative controls or as “spike-in” controls.
5.5.2.3 Attaching Probes to the Solid SurfaceThe probes are attached to a solid support or surface, which may be made, e.g., from glass, plastic (e.g., polypropylene, nylon), polyacrylamide, nitrocellulose, gel, or other porous or nonporous material. A preferred method for attaching the nucleic acids to a surface is by printing on glass plates, as is described generally by Schena et al, Science 270:467–470 (1995). This method is especially useful for preparing microarrays of cDNA (See also, DeRisi et al, Nature Genetics 14:457–460 (1996); Shalon et al., Genome Res. 6:639–645 (1996); and Schena et al., Proc. Natl. Acad. Sci. U.S.A. 93:10539–11286 (1995)).
A second preferred method for making microarrays is by making high-density oligonucleotide arrays. Techniques are known for producing arrays containing thousands of oligonucleotides complementary to defined sequences, at defined locations on a surface using photolithographic techniques for synthesis in situ (see, Fodor et al., 1991, Science 251:767–773; Pease et al., 1994, Proc. Natl. Acad. Sci. U.S.A. 91:5022–5026; Lockhart et al., 1996, Nature Biotechnology 14:1675; U.S. Pat. Nos. 5,578,832; 5,556,752; and 5,510,270) or other methods for rapid synthesis and deposition of defined oligonucleotides (Blanchard et al., Biosensors & Bioelectronics 11:687–690). When these methods are used, oligonucleotides (e.g., 60-mers) of known sequence are synthesized directly on a surface such as a derivatized glass slide. Usually, the array produced is redundant, with several oligonucleotide molecules per RNA.
Other methods for making microarrays, e.g., by masking (Maskos and Southern, 1992, Nuc. Acids. Res. 20:1679–1684), may also be used. In principle, and as noted supra, any type of array, for example, dot blots on a nylon hybridization membrane (see Sambrook et al., M
In one embodiment, the arrays of the present invention are prepared by synthesizing polynucleotide probes on a support. In such an embodiment, polynucleotide probes are attached to the support covalently at either the 3′ or the 5′ end of the polynucleotide.
In a particularly preferred embodiment, microarrays of the invention are manufactured by means of an ink jet printing device for oligonucleotide synthesis, e.g., using the methods and systems described by Blanchard in U.S. Pat. No. 6,028,189; Blanchard et al., 1996, Biosensors and Bioelectronics 11:687–690; Blanchard, 1998, in S
The polynucleotide molecules which may be analyzed by the present invention (the “target polynucleotide molecules”) may be from any clinically relevant source, but are expressed RNA or a nucleic acid derived therefrom (e.g., cDNA or amplified RNA derived from cDNA that incorporates an RNA polymerase promoter), including naturally occurring nucleic acid molecules, as well as synthetic nucleic acid molecules. In one embodiment, the target polynucleotide molecules comprise RNA, including, but by no means limited to, total cellular RNA, poly(A)+ messenger RNA (mRNA) or fraction thereof, cytoplasmic mRNA, or RNA transcribed from cDNA (i.e., cRNA; see, e.g., Linsley & Schelter, U.S. patent application Ser. No. 09/411,074, filed Oct. 4, 1999, or U.S. Pat. Nos. 5,545,522, 5,891,636, or 5,716,785). Methods for preparing total and poly(A)+ RNA are well known in the art, and are described generally, e.g., in Sambrook et al., M
In one embodiment, total RNA, mRNA, or nucleic acids derived therefrom, is isolated from a sample taken from a person afflicted with breast cancer. Target polynucleotide molecules that are poorly expressed in particular cells may be enriched using normalization techniques (Bonaldo et al., 1996, Genome Res. 6:791–806).
As described above, the target polynucleotides are detectably labeled at one or more nucleotides. Any method known in the art may be used to detectably label the target polynucleotides. Preferably, this labeling incorporates the label uniformly along the length of the RNA, and more preferably, the labeling is carried out at a high degree of efficiency. One embodiment for this labeling uses oligo-dT primed reverse transcription to incorporate the label; however, conventional methods of this method are biased toward generating 3′ end fragments. Thus, in a preferred embodiment, random primers (e.g., 9-mers) are used in reverse transcription to uniformly incorporate labeled nucleotides over the full length of the target polynucleotides. Alternatively, random primers may be used in conjunction with PCR methods or T7 promoter-based in vitro transcription methods in order to amplify the target polynucleotides.
In a preferred embodiment, the detectable label is a luminescent label. For example, fluorescent labels, bioluminescent labels, chemiluminescent labels, and colorimetric labels may be used in the present invention. In a highly preferred embodiment, the label is a fluorescent label, such as a fluorescein, a phosphor, a rhodamine, or a polymethine dye derivative. Examples of commercially available fluorescent labels include, for example, fluorescent phosphoramidites such as FluorePrime (Amersham Pharmacia, Piscataway, N.J.), Fluoredite (Millipore, Bedford, Mass.), FAM (ABI, Foster City, Calif.), and Cy3 or Cy5 (Amersham Pharmacia, Piscataway, N.J.). In another embodiment, the detectable label is a radiolabeled nucleotide.
In a further preferred embodiment, target polynucleotide molecules from a patient sample are labeled differentially from target polynucleotide molecules of a standard. The standard can comprise target polynucleotide molecules from normal individuals (i.e., those not afflicted with breast cancer). In a highly preferred embodiment, the standard comprises target polynucleotide molecules pooled from samples from normal individuals or tumor samples from individuals having sporadic-type breast tumors. In another embodiment, the target polynucleotide molecules are derived from the same individual, but are taken at different time points, and thus indicate the efficacy of a treatment by a change in expression of the markers, or lack thereof, during and after the course of treatment (i.e., chemotherapy, radiation therapy or cryotherapy), wherein a change in the expression of the markers from a poor prognosis pattern to a good prognosis pattern indicates that the treatment is efficacious. In this embodiment, different timepoints are differentially labeled.
5.5.2.5 Hybridization to MicroarraysNucleic acid hybridization and wash conditions are chosen so that the target polynucleotide molecules specifically bind or specifically hybridize to the complementary polynucleotide sequences of the array, preferably to a specific array site, wherein its complementary DNA is located.
Arrays containing double-stranded probe DNA situated thereon are preferably subjected to denaturing conditions to render the DNA single-stranded prior to contacting with the target polynucleotide molecules. Arrays containing single-stranded probe DNA (e.g., synthetic oligodeoxyribonucleic acids) may need to be denatured prior to contacting with the target polynucleotide molecules, e.g., to remove hairpins or dimers which form due to self complementary sequences.
Optimal hybridization conditions will depend on the length (e.g., oligomer versus polynucleotide greater than 200 bases) and type (e.g., RNA, or DNA) of probe and target nucleic acids. One of skill in the art will appreciate that as the oligonucleotides become shorter, it may become necessary to adjust their length to achieve a relatively uniform melting temperature for satisfactory hybridization results. General parameters for specific (i.e., stringent) hybridization conditions for nucleic acids are described in Sambrook et al., M
Particularly preferred hybridization conditions include hybridization at a temperature at or near the mean melting temperature of the probes (e.g., within 5° C., more preferably within 2° C.) in 1 M NaCl, 50 mM MES buffer (pH 6.5), 0.5% sodium sarcosine and 30% formamide.
5.5.2.6 Signal Detection and Data AnalysisWhen fluorescently labeled probes are used, the fluorescence emissions at each site of a microarray may be, preferably, detected by scanning confocal laser microscopy. In one embodiment, a separate scan, using the appropriate excitation line, is carried out for each of the two fluorophores used. Alternatively, a laser may be used that allows simultaneous specimen illumination at wavelengths specific to the two fluorophores and emissions from the two fluorophores can be analyzed simultaneously (see Shalon et al., 1996, “A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization,” Genome Research 6:639–645, which is incorporated by reference in its entirety for all purposes). In a preferred embodiment, the arrays are scanned with a laser fluorescent scanner with a computer controlled X-Y stage and a microscope objective. Sequential excitation of the two fluorophores is achieved with a multi-line, mixed gas laser and the emitted light is split by wavelength and detected with two photomultiplier tubes. Fluorescence laser scanning devices are described in Schena et al., Genome Res. 6:639–645 (1996), and in other references cited herein. Alternatively, the fiber-optic bundle described by Ferguson et al., Nature Biotech. 14:1681–1684 (1996), may be used to monitor mRNA abundance levels at a large number of sites simultaneously.
Signals are recorded and, in a preferred embodiment, analyzed by computer, e.g., using a 12 or 16 bit analog to digital board. In one embodiment the scanned image is despeckled using a graphics program (e.g., Hijaak Graphics Suite) and then analyzed using an image gridding program that creates a spreadsheet of the average hybridization at each wavelength at each site. If necessary, an experimentally determined correction for “cross talk” (or overlap) between the channels for the two fluors may be made. For any particular hybridization site on the transcript array, a ratio of the emission of the two fluorophores can be calculated. The ratio is independent of the absolute expression level of the cognate gene, but is useful for genes whose expression is significantly modulated in association with the different breast cancer-related condition.
5.6 Computer-Facilitated AnalysisThe present invention further provides for kits comprising the marker sets above. In a preferred embodiment, the kit contains a microarray ready for hybridization to target polynucleotide molecules, plus software for the data analyses described above.
The analytic methods described in the previous sections can be implemented by use of the following computer systems and according to the following programs and methods. A computer system comprises internal components linked to external components. The internal components of a typical computer system include a processor element interconnected with a main memory. For example, the computer system can be an Intel 8086-, 80386-, 80486-, Pentium™, or Pentium™-based processor with preferably 32 MB or more of main memory. The computer system may also be a Macintosh or a Macintosh-based system, but may also be a minicomputer or mainframe.
The external components may include mass storage. This mass storage can be one or more hard disks (which are typically packaged together with the processor and memory). Such hard disks are preferably of 1 GB or greater storage capacity. Other external components include a user interface device, which can be a monitor, together with an inputting device, which can be a “mouse”, or other graphic input devices, and/or a keyboard. A printing device can also be attached to the computer.
Typically, a computer system is also linked to network link, which can be part of an Ethernet link to other local computer systems, remote computer systems, or wide area communication networks, such as the Internet. This network link allows the computer system to share data and processing tasks with other computer systems.
Loaded into memory during operation of this system are several software components, which are both standard in the art and special to the instant invention. These software components collectively cause the computer system to function according to the methods of this invention. These software components are typically stored on the mass storage device. A software component comprises the operating system, which is responsible for managing computer system and its network interconnections. This operating system can be, for example, of the Microsoft Windows® family, such as Windows 3.1, Windows 95, Windows 98, Windows 2000, or Windows NT, or may be of the Macintosh OS family, or may be UNIX or an operating system specific to a minicomputer or mainframe. The software component represents common languages and functions conveniently present on this system to assist programs implementing the methods specific to this invention. Many high or low level computer languages can be used to program the analytic methods of this invention. Instructions can be interpreted during run-time or compiled. Preferred languages include C/C++, FORTRAN and JAVA. Most preferably, the methods of this invention are programmed in mathematical software packages that allow symbolic entry of equations and high-level specification of processing, including some or all of the algorithms to be used, thereby freeing a user of the need to procedurally program individual equations or algorithms. Such packages include Mathlab from Mathworks (Natick, Mass.), MATHEMATICA® software package from Wolfram Research (Champaign, IL), or S-PLUS® software package from Math Soft (Cambridge, Mass.). Specifically, the software component includes the analytic methods of the invention as programmed in a procedural language or symbolic package.
The software to be included with the kit comprises the data analysis methods of the invention as disclosed herein. In particular, the software may include mathematical routines for marker discovery, including the calculation of similarity values between clinical categories (e.g., ER status) and marker expression. The software may also include mathematical routines for calculating the similarity between sample marker expression and control marker expression, using array-generated fluorescence data, to determine the clinical classification of a sample.
Additionally, the software may also include mathematical routines for determining the prognostic outcome, and recommended therapeutic regimen, for a particular breast cancer patient. Such software would include instructions for the computer system's processor to receive data structures that include the level of expression of five or more of the marker genes listed in Table 5 in a breast cancer tumor sample obtained from the breast cancer patient; the mean level of expression of the same genes in a control or template; and the breast cancer patient's clinical information, including lymph node and ER status. The software may additionally include mathematical routines for transforming the hybridization data and for calculating the similarity between the expression levels for the marker genes in the patient's breast cancer tumor sample and the control or template. In a specific embodiment, the software includes mathematical routines for calculating a similarity metric, such as a coefficient of correlation, representing the similarity between the expression levels for the marker genes in the patient's breast cancer tumor sample and the control or template, and expressing the similarity as that similarity metric.
The software would include decisional routines that integrate the patient's clinical and marker gene expression data, and recommend a course of therapy. In one embodiment, for example, the software causes the processor unit to receive expression data for the patient's tumor sample, calculate a metric of similarity of these expression values to the values for the same genes in a template or control, compare this similarity metric to a pre-selected similarity metric threshold or thresholds that differentiate prognostic groups, assign the patient to the prognostic group, and, on the basis of the prognostic group, assign a recommended therapeutic regimen. In a specific example, the software additionally causes the processor unit to receive data structures comprising clinical information about the breast cancer patient. In a more specific example, such clinical information includes the patient's age, stage of breast cancer, estrogen receptor status, and lymph node status.
Where the control is an expression template comprising expression values for marker genes within a group of breast cancer patients, the control can comprise either hybridization data obtained at the same time (i.e., in the same hybridization experiment) as the patient's individual hybridization data, or can be a set of hybridization or marker expression values stores on a computer, or on computer-readable media. If the latter is used, new patient hybridization data for the selected marker genes, obtained from initial or follow-up tumor samples, or suspected tumor samples, can be compared to the stored values for the same genes without the need for additional control hybridizations. However, the software may additionally comprise routines for updating the control data set, i.e., to add information from additional breast cancer patients or to remove existing members of the control data set, and, consequently, for recalculating the average expression level values that comprise the template. In another specific embodiment, said control comprises a set of single-channel mean hybridization intensity values for each of said at least five of said genes, stored on a computer-readable medium.
Clinical data relating to a breast cancer patient, and used by the computer program products of the invention, can be contained in a database of clinical data in which information on each patient is maintained in a separate record, which record may contain any information relevant to the patient, the patient's medical history, treatment, prognosis, or participation in a clinical trial or study, including expression profile data generated as part of an initial diagnosis or for tracking the progress of the breast cancer during treatment.
Thus, one embodiment of the invention provides a computer program product for classifying a breast cancer patient according to prognosis, the computer program product for use in conjunction with a computer having a memory and a processor, the computer program product comprising a computer readable storage medium having a computer program mechanism encoded thereon, wherein said computer program product can be loaded into the one or more memory units of a computer and causes the one or more processor units of the computer to execute the steps of (a) receiving a first data structure comprising the level of expression of at least five of the genes for which markers are listed in Table 5 in a cell sample taken from said breast cancer patient; (b) determining the similarity of the level of expression of said at least five genes to control levels of expression of said at least five genes to obtain a patient similarity value; (c) comparing said patient similarity value to selected first and second threshold values of similarity of said level of expression of said genes to said control levels of expression to obtain first and second similarity threshold values, respectively, wherein said second similarity threshold indicates greater similarity to said control levels of expression than does said first similarity threshold; and (d) classifying said breast cancer patient as having a first prognosis if said patient similarity value exceeds said first and said second threshold similarity values, a second prognosis if said patient similarity value exceeds said first threshold similarity value but does not exceed said second threshold similarity value, and a third prognosis if said patient similarity value does not exceed said first threshold similarity value or said second threshold similarity value. In a specific embodiment of said computer program product, said first threshold value of similarity and said second threshold value of similarity are values stored in said computer. In another more specific embodiment, said first prognosis is a “very good prognosis,” said second prognosis is an “intermediate prognosis,” and said third prognosis is a “poor prognosis,” and wherein said computer program mechanism may be loaded into the memory and further cause said one or more processor units of said computer to execute the step of assigning said breast cancer patient a therapeutic regimen comprising no adjuvant chemotherapy if the patient is lymph node negative and is classified as having a good prognosis or an intermediate prognosis, or comprising chemotherapy if said patient has any other combination of lymph node status and expression profile. In another specific embodiment, said computer program mechanism may be loaded into the memory and further cause said one or more processor units of the computer to execute the steps of receiving a data structure comprising clinical data specific to said breast cancer patient. In a more specific embodiment, said clinical data includes the lymph node and estrogen receptor (ER) status of said breast cancer patient. In more specific embodiment, said single-channel hybridization intensity values are log transformed. The computer implementation of the method, however, may use any desired transformation method. In another specific embodiment, the computer program product causes said processing unit to perform said comparing step (c) by calculating the difference between the level of expression of each of said genes in said cell sample taken from said breast cancer patient and the level of expression of the same genes in said control. In another specific embodiment, the computer program product causes said processing unit to perform said comparing step (c) by calculating the mean log level of expression of each of said genes in said control to obtain a control mean log expression level for each gene, calculating the log expression level for each of said genes in a breast cancer sample from said breast cancer patient to obtain a patient log expression level, and calculating the difference between the patient log expression level and the control mean log expression for each of said genes. In another specific embodiment, the computer program product causes said processing unit to perform said comparing step (c) by calculating similarity between the level of expression of each of said genes in said cell sample taken from said breast cancer patient and the level of expression of the same genes in said control, wherein said similarity is expressed as a similarity value. In more specific embodiment, said similarity value is a correlation coefficient. The similarity value may, however, be expressed as any art-known similarity metric.
In an exemplary implementation, to practice the methods of the present invention, a user first loads experimental data into the computer system. These data can be directly entered by the user from a monitor, keyboard, or from other computer systems linked by a network connection, or on removable storage media such as a CD-ROM, floppy disk (not illustrated), tape drive (not illustrated), ZIP® drive (not illustrated) or through the network. Next the user causes execution of expression profile analysis software which performs the methods of the present invention.
In another exemplary implementation, a user first loads experimental data and/or databases into the computer system. This data is loaded into the memory from the storage media or from a remote computer, preferably from a dynamic geneset database system, through the network. Next the user causes execution of software that performs the steps of the present invention.
Additionally, because the data obtained and analyzed in the software and computer system products of the invention are confidential, the software and/or computer system comprises access controls or access control routines, such as
Alternative computer systems and software for implementing the analytic methods of this invention will be apparent to one of skill in the art and are intended to be comprehended within the accompanying claims. In particular, the accompanying claims are intended to include the alternative program structures for implementing the methods of this invention that will be readily apparent to one of skill in the art.
6. EXAMPLESMaterials And Methods
117 tumor samples from breast cancer patients were collected. RNA samples were then prepared, and each RNA sample was profiled using inkjet-printed microarrays. Marker genes were then identified based on expression patterns; these genes were then used to train classifiers, which used these marker genes to classify tumors into diagnostic and prognostic categories. Finally, these marker genes were used to predict the diagnostic and prognostic outcome for a group of individuals.
1. Sample Collection
117 breast cancer patients treated at The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands, were selected on the basis of the following clinical criteria (data extracted from the medical records of the NKI/AvL Tumor Register, Biometrics Department).
Group 1 (n=97, 78 for training, 19 for independent tests) was selected on the basis of: (1) primary invasive breast carcinoma <5 cm (T1 or T2); (2) no axillary metastases (N0); (3) age at diagnosis <55 years; (4) calender year of diagnosis 1983–1996; and (5) no prior malignancies (excluding carcinoma in situ of the cervix or basal cell carcinoma of the skin). All patients were treated by modified radical mastectomy (n=34) or breast conserving treatment (n=64), including axillary lymph node dissection. Breast conserving treatment consisted of excision of the tumor, followed by radiation of the whole breast to a dosis of 50 Gy, followed by a boost varying from 15 to 25 Gy. Five patients received adjuvant systemic therapy consisting of chemotherapy (n=3) or hormonal therapy (n=2), all other patients did not receive additional treatment. All patients were followed at least annually for a period of at least 5 years. Patient follow-up information was extracted from the Tumor Registry of the Biometrics Department.
Group 2 (n=20) was selected as: (1) carriers of a germline mutation in BRCA1 or BRCA2; and (2) having primary invasive breast carcinoma. No selection or exclusion was made based on tumor size, lymph node status, age at diagnosis, calender year of diagnosis, other malignancies. Germline mutation status was known prior to this research protocol.
Information about individual from which tumor samples were collected include: year of birth; sex; whether the individual is pre- or post-menopausal; the year of diagnosis; the number of positive lymph nodes and the total number of nodes; whether there was surgery, and if so, whether the surgery was breast-conserving or radical; whether there was radiotherapy, chemotherapy or hormonal therapy. The tumor was graded according to the formula P=TNM, where T is the tumor size (on a scale of 0–5); N is the number of nodes that are positive (on a scale of 0–4); and M is metastases (0=absent, 1=present). The tumor was also classified according to stage, tumor type (in situ or invasive; lobular or ductal; grade) and the presence or absence of the estrogen and progesterone receptors. The progression of the cancer was described by (where applicable): distant metastases; year of distant metastases, year of death, year of last follow-up; and BRCA1 genotype.
2. Tumors:
Germline mutation testing of BRCA1 and BRCA2 on DNA isolated from peripheral blood lymphocytes includes mutation screening by a Protein Truncation Test (PTT) of exon 11 of BRCA1 and exon 10 and 11 of BRCA2, deletion PCR of BRCA1 genomic deletion of exon 13 and 22, as well Denaturing Gradient Gel Electrophoresis (DGGE) of the remaining exons. Aberrant bands were all confirmed by genomic sequencing analyzed on a ABI3700 automatic sequencer and confirmed on a independent DNA sample.
From all, tumor material was snap frozen in liquid nitrogen within one hour after surgery. Of the frozen tumor material an H&E (hematoxylin-eosin) stained section was prepared prior to and after cutting slides for RNA isolation. These H&E frozen sections ere assessed for the percentage of tumor cells; only samples with >50% tumor cells were selected for further study.
For all tumors, surgical specimens fixed in formaldehyde and embedded in paraffin were evaluated according to standard histopathological procedures. H&E stained paraffin sections were examined to assess tumor type (e.g., ductal or lobular according to the WHO classification); to assess histologic grade according the method described by Elston and Ellis (grade 1–3); and to assess the presence of lymphangio-invasive growth and the presence of an extensive lymphocytic infiltrate. All histologic factors were independently assessed by two pathologists (MV and JL); consensus on differences was reached by examining the slides together. A representative slide of each tumor was used for immunohistochemical staining with antibodies directed against the estrogen- and progesterone receptor by standard procedures. The staining result was scored as the percentage of positively staining nuclei (0%, 10%, 20%, etc., up to 100%).
3. Amplification, Labeling, and Hybridization
The outline for the production of marker-derived nucleic acids and hybridization of the nucleic acids to a microarray are outlined in
5 μg total RNA was used as input for cRNA synthesis. An oligo-dT primer containing a T7 RNA polymerase promoter sequence was used to prime first strand cDNA synthesis, and random primers (pdN6) were used to prime second strand cDNA synthesis by MMLV reverse transcriptase. This reaction yielded a double-stranded cDNA that contained the T7 RNA polymerase (T7RNAP) promoter. The double-stranded cDNA was then transcribed into cRNA by T7RNAP.
cRNA was labeled with Cy3 or Cy5 dyes using a two-step process. First, allylamine-derivatized nucleotides were enzymatically incorporated into cRNA products. For cRNA labeling, a 3:1 mixture of 5-(3-Aminoallyl)uridine 5′-triphosphate (Sigma) and UTP was substituted for UTP in the in vitro transcription (IVT) reaction. Allylamine-derivatized cRNA products were then reacted with N-hydroxy succinimide esters of Cy3 or Cy5 (CyDye, Amersham Pharmacia Biotech). 5 μg Cy5-labeled cRNA from one breast cancer patient was mixed with the same amount of Cy3-labeled product from a pool of equal amount of cRNA from each individual sporadic patient.
Microarray hybridizations were done in duplicate with fluor reversals. Before hybridization, labeled cRNAs were fragmented to an average size of 50–100nt by heating at 60° C. in the presence of 10 mM ZnCl2. Fragmented cRNAs were added to hybridization buffer containing 1 M NaCl, 0.5% sodium sarcosine and 50 mM MES, pH 6.5, which stringency was regulated by the addition of formamide to a final concentration of 30%. Hybridizations were carried out in a final volume of 3 ml at 40° C. on a rotating platform in a hybridization oven (Robbins Scientific) for 48 h. After hybridization, slides were washed and scanned using a confocal laser scanner (Agilent Technologies). Fluorescence intensities on scanned images were quantified, normalized and corrected.
4. Pooling of Samples
The reference cRNA pool was formed by pooling equal amount of cRNAs from each individual sporadic patient, for a total of 78 tumors.
5. 25 k Human Microarray
Surface-bound oligonucleotides were synthesized essentially as proposed by Blanchard et al., Biosens. Bioelectron. 6(7):687–690 (1996); see also Hughes et al., Nature Biotech. 19(4):342–347 (2000). Hydrophobic glass surfaces (3 inches by 3 inches) containing exposed hydroxyl groups were used as substrates for nucleotide synthesis. Phosphoramidite monomers were delivered to computer-defined positions on the glass surfaces using ink-jet printer heads. Unreacted monomers were then washed away and the ends of the extended oligonucleotides were deprotected. This cycle of monomer coupling, washing and deprotection was repeated for each desired layer of nucleotide synthesis. Oligonucleotide sequences to be printed were specified by computer files.
Microarrays containing approximately 25,000 human gene sequences (Hu25K microarrays) were used for this study. Sequences for microarrays were selected from RefSeq (a collection of non-redundant mRNA sequences, located on the Internet and Phil Green EST contigs, which is a collection of EST contigs assembled by Dr. Phil Green et al at the University of Washington (Ewing and Green, Nat. Genet. 25(2):232–4 (2000)), available on the Internet. Each mRNA or EST contig was represented on Hu25K microarray by a single 60mer oligonucleotide essentially as described in Hughes et al., Nature Biotech. 19(4):342–347 and in International Publication WO 01/06013, published Jan. 25, 2001, and in International Publication WO 01/05935, published Jan. 25, 2001, except that the rules for oligo screening were modified to remove oligonucleotides with more than 30% C or with 6 or more contiguous C residues.
Example 1Differentially Regulated Gene Sets and Overall Expression Patterns of Breast Cancer Tumors
Of the approximately 25,000 sequences represented on the microarray, a group of approximately 5,000 genes that were significantly regulated across the group of samples was selected. A gene was determined to be significantly differentially regulated with cancer of the breast if it showed more than two-fold of transcript changes as compared to a sporadic tumor pool, and if the p-value for differential regulation (Hughes et al., Cell 102:109–126 (2000)) was less than 0.01 either upwards or downwards in at least five out of 98 tumor samples.
An unsupervised clustering algorithm allowed us to cluster patients based on their similarities measured over this set of ˜5,000 significant genes. The similarity between two patients x and y is defined as
In Equation (5), X and Y are two patients with components of log ratio xi and yi, i=1, . . . , N=5,100. Associated with every value xi is error σx
is the error-weighted arithmetic mean.
The use of correlation as similarity metric emphasizes the importance of co-regulation in clustering rather than the amplitude of regulations.
The set of approximately 5,000 genes can be clustered based on their similarities measured over the group of 98 tumor samples. The similarity between two genes was defined in the same way as in Equation (1) except that now for each gene, there are 98 components of log ratio measurements.
The result of such a two-dimensional clustering is displayed in
To help understand these patterns, they were associated with estrogen-receptor (ER), proestrogen receptor (PR), tumor grade, presence of lymphocytic infiltrate, 2 and angioinvasion (
From
Identification of Genetic Markers Distinguishing Estrogen Receptor (+) From Estrogen Receptor (−) Patients
The results described in this Example allow the identification of expression marker genes that differentiate two major types of tumor cells: “ER-negative” group and “ER-positive” group. The differentiation of samples by ER(+) status was accomplished in our steps: (1) identification of a set of candidate marker genes that correlate with ER level; 2) rank-ordering these candidate genes by strength of correlation; (3) optimization of the number of marker genes; and (4) classifying samples based on these marker genes.
1. Selection of Candidate Discriminating Genes
In the first step, a set of candidate discriminating genes was identified based on gene expression data of training samples. Specifically, we calculated the correlation coefficients ρ between the category numbers or ER level and logarithmic expression ratio {right arrow over (r)} across all the samples for each individual gene:
ρ=({right arrow over (c)}·{right arrow over (r)})/(∥{right arrow over (c)}∥·∥{right arrow over (r)}∥) Equation (2)
The histogram of resultant correlation coefficients is shown in
Genes having a correlation coefficient larger than 0.3 (“correlated genes”) or less than −0.3 (“anti-correlated genes”) were selected as reporter genes. The threshold of 0.3 was selected based on the correlation distribution for cases where there is no real correlation (one can use permutations to determine this distribution). Statistically, this distribution width depends upon the number of samples used in the correlation calculation. The distribution width for control cases (no real correlation) is approximately 1/√{square root over (n−3)}, where n=the number of samples. In our case, n=98. Therefore, a threshold of 0.3 roughly corresponds to 3−σ in the distribution (3×1/√{square root over (n−3)}).
2,460 such genes were found to satisfy this criterion. In order to evaluate the significance of the correlation coefficient of each gene with the ER level, a bootstrap technique was used to generate Monte-Carlo data that randomize the association between gene expression data of the samples and their categories. The distribution of correlation coefficients obtained from one Monte-Carlo trial is shown as a dashed line in
2. Rank-Ordering of Candidate Discriminating Genes
In the second step, genes on the candidate list were rank-ordered based on the significance of each gene as a discriminating gene. The markers were rank-ordered either by amplitude of correlation, or by using a metric similar to a Fisher statistic:
t=(<x1>−<x2>)/√{square root over ([σ12(n1−1)+σ22(n2−1)]/(n1+n2−1)/(1/n1+1/n2))}{square root over ([σ12(n1−1)+σ22(n2−1)]/(n1+n2−1)/(1/n1+1/n2))}{square root over ([σ12(n1−1)+σ22(n2−1)]/(n1+n2−1)/(1/n1+1/n2))}{square root over ([σ12(n1−1)+σ22(n2−1)]/(n1+n2−1)/(1/n1+1/n2))} Equation (3)
In Equation (3), <x1> is the error-weighted average of log ratio within the ER(−), and <x2> is the error-weighted average of log ratio within the ER(+) group. σ1 is the variance of log ratio within the ER(−) group and n1 is the number of samples that had valid measurements of log ratios. σ2 is the variance of log ratio within the ER(+) group and n2 is the number of samples that had valid measurements of log ratios. The t-value in Equation (3) represents the variance-compensated difference between two means. The confidence level of each gene in the candidate list was estimated with respect to a null hypothesis derived from the actual data set using a bootstrap technique; that is, many artificial data sets were generated by randomizing the association between the clinical data and the gene expression data.
3. Optimization of the Number of Marker Genes
The leave-one-out method was used for cross validation in order to optimize the discriminating genes. For a set of marker genes from the rank-ordered candidate list, a classifier was trained with 97 samples, and was used to predict the status of the remaining sample. The procedure was repeated for each of the samples in the pool, and the number of cases where the prediction for the one left out is wrong or correct was counted.
The above performance evaluation from leave-one-out cross validation was repeated by successively adding more marker genes from the candidate list. The performance as a function of the number of marker genes is shown in
4. Classification Based on Marker Genes
In the third step, a set of classifier parameters was calculated for each type of training data set based on either of the above ranking methods. A template for the ER(−) group ({right arrow over (z)}1) was generated using the error-weighted log ratio average of the selected group of genes. Similarly, a template for ER(+) group (called {right arrow over (z)}2) was generated using the error-weighted log ratio average of the selected group of genes. Two classifier parameters (P1 and P2) were defined based on either correlation or distance. P measures the similarity between one sample {right arrow over (y)} and the ER(−) template {right arrow over (z)}1 over this selected group of genes. P2 measures the similarity between one sample {right arrow over (y)} and the ER(+) template {right arrow over (z)}2 over this selected group of genes. The correlation Pi is defined as:
Pi=({right arrow over (z)}i·{right arrow over (y)})/(∥{right arrow over (z)}i∥·∥{right arrow over (y)}∥) Equation (1)
A “leave-one-out” method was used to cross-validate the classifier built based on the marker genes. In this method, one sample was reserved for cross validation each time the classifier was trained. For the set of 550 optimal marker genes, the classifier was trained with 97 of the 98 samples, and the status of the remaining sample was predicted. This procedure was performed with each of the 98 patients. The number of cases where the prediction was wrong or correct was counted. It was further determined that subsets of as few as ˜50 of the 2,460 genes are able classify tumors as ER(+) or ER(−) nearly as well as using the total set.
In a small number of cases, there was disagreement between classification by the 550 marker set and a clinical classification. In comparing the microarray measured log ratio of expression for ESR1 to the clinical binary decision (negative or positive) of ER status for each patient, it was seen that the measured expression is consistent with the qualitative category of clinical measurements (mixture of two methods) for the majority of tumors. For example, two patients who were clinically diagnosed as ER(+) actually exhibited low expression of ESR1 from microarray measurements and were classified as ER negative by 550 marker genes. Additionally, 3 patients who were clinically diagnosed as ER(−) exhibited high expression of ESR1 from microarray measurements and were classified as ER(+) by the same 550 marker genes. Statistically, however, microarray measured gene expression of ESR1 correlates with the dominant pattens better than clinically determined ER status.
Example 3Identification of Genetic Markers Distinguishing BRCA1 Tumors From Sporadic Tumors in Estrogen Receptor (−) Patients
The BRCA1 mutation is one of the major clinical categories in breast cancer tumors. It was determined that of tumors of 38 patients in the ER(−) group, 17 exhibited the BRCA1 mutation, while 21 were sporadic tumors. A method was therefore developed that enabled the differentiation of the 17 BRCA1 mutation tumors from the 21 sporadic tumors in the ER(−) group.
1. Selection of candidate discriminating genes
In the first step, a set of candidate genes was identified based on the gene expression patterns of these 38 samples. We first calculated the correlation between the BRCA1-mutation category number and the expression ratio across all 38 samples for each individual gene by Equation (2). The distribution of the correlation coefficients is shown as a histogram defined by the solid line in
In order to evaluate the significance of each correlation coefficient with respect to a null hypothesis that such correlation coefficient could be found by chance, a bootstrap technique was used to generate Monte-Carlo data that randomizes the association between gene expression data of the samples and their categories. 10,000 such Monte-Carlo runs were generated as a control in order to estimate the significance of the marker genes as a group. A threshold of 0.35 in the absolute amplitude of correlation coefficients (either correlation or anti-correlation) was applied both to the real data and the Monte-Carlo data. Following this method, 430 genes were found to satisfy this criterion for the experimental data. The p-value of the significance, as measured against the 10,000 Monte-Carlo trials, is approximately 0.0048 (
2. Rank-Ordering of Candidate Discriminating Genes
In the second step, genes on the candidate list were rank-ordered based on the significance of each gene as a discriminating gene. Here, we used the absolute amplitude of correlation coefficients to rank order the marker genes.
3 Optimization of Discriminating Genes
In the third step, a subset of genes from the top of this rank-ordered list was used for classification. We defined a BRCA1 group template (called {right arrow over (z)}1) by using the error-weighted log ratio average of the selected group of genes. Similarly, we defined a non-BRCA1 group template (called {right arrow over (z)}2) by using the error-weighted log ratio average of the selected group of genes. Two classifier parameters (P1 and P2) were defined based on either correlation or distance. P1 measures the similarity between one sample Y and the BRCA1 template {right arrow over (z)}1 over this selected group of genes. P2 measures the similarity between one sample {right arrow over (y)} and the non-BRCA1 template {right arrow over (z)}2 over this selected group of genes. For correlation, P1 and P2 were defined in the same way as in Equation (4).
The leave-one-out method was used for cross validation in order to optimize the discriminating genes as described in Example 2. For a set of marker genes from the rank-ordered candidate list, the classifier was trained with 37 samples the remaining one was predicted. The procedure was repeated for all the samples in the pool, and the number of cases where the prediction for the one left out is wrong or correct was counted.
To determine the number of markers constituting a viable subset, the above performance evaluation from leave-one-out cross validation was repeated by cumulatively adding more marker genes from the candidate list. The performance as a function of the number of marker genes is shown in
The classification results using the optimal 100 genes are shown in
Identification of Genetic Markers Distinguishing Sporadic Tumor Patients with >5 Year Versus <5 Year Survival Times
78 tumors from sporadic breast cancer patients were used to explore prognostic predictors from gene expression data. Of the 78 samples in this sporadic breast cancer group, 44 samples were known clinically to have had no distant metastases within 5 years since the initial diagnosis (“no distant metastases group”) and 34 samples had distant metastases within 5 years since the initial diagnosis (“distant metastases group”). A group of 231 markers, and optimally a group of 70 markers, was identified that allowed differentiation between these two groups.
1. Selection of Candidate Discriminating Genes
In the first step, a set of candidate discriminating genes was identified based on gene expression data of these 78 samples. The correlation between the prognostic category number (distant metastases vs no distant metastases) and the logarithmic expression ratio across all samples for each individual gene was calculated using Equation (2). The distribution of the correlation coefficients is shown as a solid line in
In order to evaluate the significance of each correlation coefficient with respect to a null hypothesis that such correlation coefficient can be found by chance, we used a bootstrap technique to generate data from 10,000 Monte-Carlo runs as a control (
2. Rank-Ordering of Candidate Discriminating Genes
In the second step, genes on the candidate list were rank-ordered based on the significance of each gene as a discriminating gene. Specifically, a metric similar to a “Fisher” statistic, defined in Equation (3), was used for the purpose of rank ordering. The confidence level of each gene in the candidate list was estimated with respect to a null hypothesis derived from the actual data set using the bootstrap technique. Genes in the candidate list can also be ranked by the amplitude of correlation coefficients.
3. Optimization of Discriminating Genes
In the third step, a subset of 5 genes from the top of this rank-ordered list was selected to use as discriminating genes to classify 78 tumors into a “distant metastases group” or a “no distant metastases group”. The leave-one-out method was used for cross validation. Specifically, 77 samples defined a classifier based on the set of selected discriminating genes, and these were used to predict the remaining sample. This procedure was repeated so that each of the 78 samples was predicted. The number of cases in which predictions were correct or incorrect were counted. The performance of the classifier was measured by the error rates of type 1 and type 2 for this selected gene set.
We repeated the above performance evaluation procedure, adding 5 more marker genes each time from the top of the candidate list, until all 231 genes were used. As shown in
4. Reoccurrence Probability Curves
The prognostic classification of 78 patients with sporadic breast cancer tumors into two distinct subgroups was predicted based on their expression of the 70 optimal marker genes (
To evaluate the prognostic classification of sporadic patients, we predicted the outcome of each patient by a classifier trained by the remaining 77 patients based on the 70 optimal marker genes.
To parameterize the reoccurrence probability as a function of time since initial diagnosis, the curve was fitted to one type of survival model—“normal”:
P=α×exp(−t2/τ2) (4)
For fixed α=1, we found that τ=125 months for patients in the no distant metastases group and τ=36 months for patients in the distant metastases group. Using tumor grades, we found τ=100 months for patients with tumor grades 1 and 2 and τ=60 for patients with tumor grade 3. It is accepted clinical practice that tumor grades are the best available prognostic predictor. However, the difference between the two prognostic groups classified based on 70 marker genes is much more significant than those classified by the best available clinical information.
5. Prognostic Prediction for 19 Independent Sporadic Tumors
To confirm the proposed prognostic classification method and to ensure the reproducibility, robustness, and predicting power of the 70 optimal prognostic marker genes, we applied the same classifier to 19 independent tumor samples from sporadic breast cancer patients, prepared separately at The Netherlands Cancer Institute (NKI). The same reference pool was used.
The classification results of 19 independent sporadic tumors are shown in
6. Clinical Parameters as a Group vs. Microarray Data—Results of Logistic Regression
In the previous section, the predictive power of each individual clinical parameter was compared with that of the expression data. However, it is more meaningful to combine all the clinical parameters as a group, and then compare them to the expression data. This requires multi-variant modeling; the method chosen was logistic regression. Such an approach also demonstrates how much improvement the microarray approach adds to the results of the clinical data.
The clinical parameters used for the multi-variant modeling were: (1) tumor grade; (2) ER status; (3) presence or absence of the progestogen receptor (PR); (4) tumor size; (5) patient age; and (6) presence or absence of angioinvasion. For the microarray data, two correlation coefficients were used. One is the correlation to the mean of the good prognosis group (C1) and the other is the correlation to the mean of the bad prognosis group (C2). When calculating the correlation coefficients for a given patient, this patient is excluded from either of the two means.
The logistic regression optimizes the coefficient of each input parameter to best predict the outcome of each patient. One way to judge the predictive power of each input parameter is by how much deviance (similar to Chi-square in the linear regression, see for example, Hasomer & Lemeshow, A
The clinical parameters, and the two microarray parameters, were then monitored as a group. The total deviance explained by the six clinical parameters was 31.5, and total deviance explained by the microarray parameters was 39.4. However, when the clinical data was modeled first, and the two microarray parameters added, the final deviance accounted for is 57.0.
The logistic regression computes the likelihood that a patient belongs to the good or poor prognostic group.
Odds ratio tables can be created from the prediction of the logistic regression. The probability of a patient being in the good prognosis group is calculated by the logistic regression based on different combinations of input parameters (clinical and/or microarray). Patients are divided into the following four groups according to the prediction and the true outcome: (1) predicted good and truly good, (2) predicted good but truly poor, (3) predicted poor but truly good, (4) predicted poor and truly poor. Groups (1) & (4) represent correct predictions, while groups (2) & (3) represent mis-predictions. The division for the prediction is set at probability of 50%, although other thresholds can be used. The results are listed in Table 8. It is clear from Table 8 that microarray profiling (Table 8.3 & 8.10) outperforms any single clinical data (Table 8.4–8.9) and the combination of the clinical data (Table 8.2). Adding the micro-array profiling in addition to the clinical data give the best results (Table 8.1).
For microarray profiling, one can also make a similar table (Table 8.11) without using logistic regression. In this case, the prediction was simply based on C1-C2 (greater than 0 means good prognosis, less than 0 mean poor prognosis).
Concept of Mini-Array for Diagnosis Purposes
All genes on the marker gene list for the purpose of diagnosis and prognosis can be synthesized on a small-scale microarray using ink-jet technology. A microarray with genes for diagnosis and prognosis can respectively or collectively be made. Each gene on the list is represented by single or multiple oligonucleotide probes, depending on its sequence uniqueness across the genome. This custom designed mini-array, in combination with sample preparation protocol, can be used as a diagnostic/prognostic kit in clinics.
Example 6Biological Significance of Diagnostic Marker Genes
The public domain was searched for the available functional annotations for the 430 marker genes for BRCA1 diagnosis in Table 3. The 430 diagnostic genes in Table 3 can be divided into two groups: (1) 196 genes whose expressions are highly expressed in BRCA1-like group; and (2) 234 genes whose expression are highly expressed sporadic group. Of the 196 BRCA1 group genes, 94 are annotated. Of the 234 sporadic group genes, 100 are annotated. The terms “T-cell”, “B-cell” or “immunoglobulin” are involved in 13 of the 94 annotated genes, and in 1 of the 100 annotated genes, respectively. Of 24,479 genes represented on the microarrays, there are 7,586 genes with annotations to date. “T-cell”, B-cell” and “immunoglobulin” are found in 207 of these 7,586 genes. Given this, the p-value of the 13 “T-cell”, “B-cell” or “immunoglobulin” genes in the BRCA1 group is very significant (p-value=1.1×10−6). In comparison, the observation of 1 gene relating to “T-cell”, “B-cell”, or “immunoglobulin” in the sporadic group is not significant (p-value=0.18).
The observation that BRCA1 patients have highly expressed lymphocyte (T-cell and B-cell) genes agrees with what has been seen from pathology that BRCA1 breast tumor has more frequently associated with high lymphocytic infiltration than sporadic cases (Chappuis et al., 2000, Semin Surg Oncol 18:287–295).
Example 7Biological Significance of Prognosis Marker Genes
A search was performed for available functional annotations for the 231 prognosis marker genes (Table 5). The markers fall into two groups: (1) 156 markers whose expressions are highly expressed in poor prognostic group; and (2) 75 genes whose expression are highly expressed in good prognostic group. Of the 156 markers, 72 genes are annotated; of the 75 genes, 28 genes are annotated.
Twelve of the 72 markers, but none of the 28 markers, are, or are associated with, kinases. In contrast, of the 7,586 genes on the microarray having annotations to date, only 471 involve kinases. On this basis, the p-value that twelve kinase-related markers in the poor prognostic group is significant (p-value =0.001). Kinases are important regulators of intracellular signal transduction pathways mediating cell proliferation, differentiation and apoptosis. Their activity is normally tightly controlled and regulated. Overexpression of certain kinases is well known involving in oncogenesis, such as vascular endothelial growth factor receptor1 (VEGFR1 or FLT1), a tyrosine kinase in the poor prognosis group, which lays a very important role in tumor angiogenesis. Interestingly, vascular endothelial growth factor (VEGF), VEGFR's ligand, is also found in the prognosis group, which means both ligand and receptor are upregulated in poor prognostic individuals by an unknown mechanism.
Likewise, 16 of the 72 markers, and only two of the 28 markers, are, or are associated with, ATP-binding or GTP-binding proteins. In contrast, of the 7,586 genes on the microarray having annotations to date, only 714 and 153 involve ATP-binding and GTP-binding, respectively. On this basis, the p-value that 16 GTP- or ATP-binding-related markers in the poor prognosis group is significant (p-value 0.001 and 0.0038). Thus, the kinase- and ATP— or GTP-binding-related markers within the 72 markers can be used as prognostic indicators.
Cancer is characterized by deregulated cell proliferation. On the simplest level, this requires division of the cell or mitosis. By keyword searching, we found “cell division” or “mitosis” included in the annotations of 7 genes respectively in the 72 annotated markers from the 156 poor prognosis markers, but in none for the 28 annotated genes from 75 good prognosis markers. Of the 7,586 microarray markers with annotations, “cell division” is found in 62 annotations and “mitosis” is found in 37 annotations. Based on these findings, the p-value that seven cell division- or mitosis-related markers are found in the poor prognosis group is estimated to be highly significant (p-value=3.5×10−5). In comparison, the absence of cell division- or mitosis-related markers in the good prognosis group is not significant (p-value =0.69). Thus, the seven cell division- or mitosis-related markers may be used as markers for poor prognosis.
Example 8Construction of an Artificial Reference Pool
The reference pool for expression profiling in the above Examples was made by using equal amount of cRNAs from each individual patient in the sporadic group. In order to have a reliable, easy-to-made, and large amount of reference pool, a reference pool for breast cancer diagnosis and prognosis can be constructed using synthetic nucleic acid representing, or derived from, each marker gene. Expression of marker genes for individual patient sample is monitored only against the reference pool, not a pool derived from other patients.
To make the reference pool, 60-mer oligonucleotides are synthesized according to 60-mer ink-jet array probe sequence for each diagnostic/prognostic reporter genes, then double-stranded and cloned into pBluescript SK-vector (Stratagene, La Jolla, Calif.), adjacent to the T7 promoter sequence. Individual clones are isolated, and the sequences of their inserts are verified by DNA sequencing. To generate synthetic RNAs, clones are linearized with EcoRI and a T7 in vitro transcription (IVT) reaction is performed according to the MegaScript kit (Ambion, Austin, Tex.). IVT is followed by DNase treatment of the product. Synthetic RNAs are purified on RNeasy columns (Qiagen, Valencia, Calif.). These synthetic RNAs are transcribed, amplified, labeled, and mixed together to make the reference pool. The abundance of those synthetic RNAs are adjusted to approximate the abundance of the corresponding marker-derived transcripts in the real tumor pool.
Example 9Use of Single-Channel Data and a Sample Pool Represented by Stored Values
1. Creation of a Reference Pool of Stored Values (“Mathematical Sample Pool”)
The use of ratio-based data used in Examples 1–7, above, requires a physical reference sample. In the above Examples, a pool of sporadic tumor sample was used as the reference. Use of such a reference, while enabling robust prognostic and diagnostic predictions, can be problematic because the pool is typically a limited resource. A classifier method was therefore developed that does not require a physical sample pool, making application of this predictive and diagnostic technique much simpler in clinical applications.
To test whether single-channel data could be used, the following procedure was developed. First, the single channel intensity data for the 70 optimal genes, described in Example 4, from the 78 sporadic training samples, described in the Materials and Methods, was selected from the sporadic sample vs. tumor pool hybridization data. The 78 samples consisted of 44 samples from patients having a good prognosis and 34 samples from patients having a poor prognosis. Next, the hybridization intensities for these samples were normalized by dividing by the median intensity of all the biological spots on the same microarray. Where multiple microarrays per sample were used, the average was taken across all of the microarrays. A log transform was performed on the intensity data for each of the 70 genes, or for the average intensity for each of the 70 genes where more than one microarray is hybridized, and a mean log intensity for each gene across the 78 sporadic samples was calculated. For each sample, the mean log intensities thus calculated were subtracted from the individual sample log intensity. This figure, the mean subtracted log(intensity) was then treated as the two color log(ratio) for the classifier by substitution into Equation (5). For new samples, the mean log intensity is subtracted in the same manner as noted above, and a mean subtracted log(intensity) calculated.
The creation of a set of mean log intensities for each gene hybridized creates a “mathematical sample pool” that replaces the quantity-limited “material sample pool.” This mathematical sample pool can then be applied to any sample, including samples in hand and ones to be collected in the future. This “mathematical sample pool” can be updated as more samples become available.
2. Results
To demonstrate that the mathematical sample pool performs a function equivalent to the sample reference pool, the mean-subtracted-log(intensity) (single channel data, relative to the mathematical pool) vs. the log(ratio) (hybridizations, relative to the sample pool) was plotted for the 70 optimal reporter genes across the 78 sporadic samples, as shown in
As shown in
In clinical applications, it is greatly preferable to have few false negatives, which results in fewer under-treated patients. To conform the results to this preference, a classifier was constructed by ranking the patient sample according to its coefficient of correlation to the “good prognosis” template, and choosing a threshold for this correlation coefficient to allow approximately 10% false negatives, i.e., classification of a sample from a patient with poor prognosis as one from a patient with a good prognosis. Out of the 34 poor prognosis samples used herein, this represents a tolerance of 3 out of 34 poor prognosis patients classified incorrectly. This tolerance limit corresponds to a threshold 0.2727 coefficient of correlation to the “good prognosis” template. Results using this threshold are shown in
In summary, the 70 reporter genes carry robust information about prognosis; the single channel data can predict the tumor outcome almost as well as the ratio based data, while being more convenient in a clinical setting.
Example 10Comparison of Predictive Power of 70 Optimal Genes to Clinical Predictors and Development of Three Prognosis Categories
Using inkjet-synthesized oligonucleotide microarrays, we have defined a gene expression profile associated with prognosis in breast cancer. To identify this gene expression profile, tumors of less than 5 cm from lymph node negative patients younger than 55 years were used. Surprisingly, a 70 gene-based classifier outperformed all clinical parameters in predicting distant metastases within 5 years. The odds ratio for metastases of the “poor prognosis” versus “good prognosis” signature group based on the gene expression pattern was estimated to be approximately 15 by a cross-validation procedure. Even though these results were highly encouraging, a limitation of this first study was that these results were derived from and tested on two groups of patients which were selected for outcome: one group of patients who developed distant metastases within 5 years and one group of patients who remained disease-free for at least 5 years.
To provide a more accurate estimate of the risk of metastases associated with the prognosis signature and to further substantiate that the gene expression profile is a clinically meaningful tool, a cohort of 295 young breast cancer patients including both lymph node negative and positive patients was studied. The findings confirm that the prognosis profile is a more powerful predictor of disease outcome than currently used criteria.
1. Breast Tumor Selection Criteria
A consecutive series of 295 tumors was selected from The Netherlands Cancer Institute (NKI) fresh-frozen tissue bank according to the following patient selection criteria: primary invasive breast carcinoma less than 5 cm at pathologic examination (pTI or pT2); tumor-negative apical axillary lymph node as determined by a negative infraclavicular lymph node biopsy; age at diagnosis 52 years or younger; calendar year of diagnosis 1984–1995; and no prior malignancies. All patients had been treated by modified radical mastectomy or breast conserving surgery, including axillary lymph node dissection, followed by radiotherapy if indicated. The 295 tumor samples included 151 taken from lymph node negative (pathologic examination pN0) patients and 144 lymph node positive (pN+) patients. Ten of the 151 lymph node negative patients and 120 of the 144 lymph node positive patients had received adjuvant systemic therapy consisting of chemotherapy (n=90), hormonal therapy (n=20), or both (n=20). All patients were followed at least annually for a period of at least 5 years. Patient follow-up information was extracted from the NKI Medical Registry. Median follow-up of the 207 patients without metastases as first event was 7.8 years (range: 0.05–18.3) versus 2.7 years (0.3–14.0) for the 88 patients with metastasis as first event during follow-up. For all 295 patients median follow-up is 6.7 years (0.05–18.3). There were no missing data. This study was approved by the Medical Ethical Committee of the Netherlands Cancer Institute.
Clinicopathological parameters were determined as described in Materials and Methods, above. Estrogen receptor (ER) expression was estimated by hybridization intensity obtained from microarray experiments. Using this assay, it was determined that the cohort of 295 tumor samples includes 69 ER negative (ERα log10 intensity ratio below −0.65 units, corresponding to less than 10% nuclei with positive staining by immunohistochemistry) and 226 ER positive tumors. Histological grade was assessed using the method described by Elston and Ellis, Histopathol. 19(5):403–410 (1991). Vascular invasion was assessed as none (−); minor (1–3 vessels; +/−); major (>3 vessels).
2. RNA Isolation and Microarray Expression Profiling
RNA isolation, cRNA labeling, the 25K oligonucleotide microarrays, and hybridization experiments were as described in Materials and Methods. The statistical error model that assigns p values to expression ratios was as described in Example 4. After hybridization, slides were washed and scanned using a confocal laser scanner (Agilent Technologies) (see Hughes et al., Nat. Biotechnol. 19(4):342–347 (2001)).
3. Correlation of the Microarray Data with the Previously Determined Prognosis Profile
The prognostic value of the gene expression profile in a consecutive series of breast cancer patients was determined using the 70 marker genes identified in the experiments described in Example 4. To acquire this consecutive series, 61 of the pN0 patients that were also part of the training series used for the construction of the 70-gene prognosis profile were also included. Leaving out these patients would have resulted in selection bias, because the first series contained a disproportionally large number of patients who developed distant metastases within 5 years. For each of the 234 new tumors in this 295 tumor sample cohort we calculated the correlation coefficient of the expression of the 70 genes with the previously determined average profile of these genes in tumors of good prognosis patients (C1) (see Example 4). A tumor with a correlation coefficient >0.4 (a threshold previously determined in the training set of 78 tumors that allowed 10% false negatives) was then assigned to the “good prognosis” signature group and all other tumors were assigned to the “poor prognosis” signature group. To avoid overfitting by the 61 previously used pN0 patients, the performance cross-validated correlation coefficients were used for the prognosis classification with a threshold correlation coefficient value of 0.55 (corresponding to the threshold for 10% false negatives of this cross-validated classifier).
4. Statistical Analysis
In the analysis of distant metastasis-free probabilities, patients whose first event was distant metastases were counted as failures; all other patients were censored at the date of their last follow-up, non-breast cancer death, local-regional recurrence or second primary malignancy, including contralateral breast cancer. Time was measured from the date of surgery. Metastasis-free curves were drawn using the method of Kaplan and Meier and compared using the log-rank test. Standard errors (SEs) of the metastasis-free percentages were calculated using the method of Tsiatis (Klein, Scand. J of Statistics 18:333–340 (1991)).
Proportional hazard regression analysis (Cox, J. R. Statist. Soc. B 34:187–220 (1972)) was used to adjust the association between the correlation coefficient C1 and metastases for other variables. SE's were calculated using the sandwich estimator (Lin and Wei, J. Amer. Stat. Assoc. 84:1074–1079 (1989)). Histological grade, vascular invasion and the number of axillary lymph node metastases (0 vs. 1–3 vs. ≧4) were used as variables. Linearity of the relation between In (relative hazard) and tumor diameter, age and expression level of ER was tested using the Wald test for non-linear components of restricted cubic splines (Themeau et al., Biometrika 77:147–160 (1990)). No evidence for non-linearity was found (age: p=0.83, tumor diameter: p=0.75, number of positive nodes: p=0.65 and ER expression level: p=0.27). Non-proportionality of the hazard was tested using the Grambsch and Themeau method (Grambsch and Themeau, Biometrika 81:515–526 (1994)). In addition, for C1 the difference between the ln(hazard ratio) before and after 5 years of follow-up was tested using the Wald test. All calculations were done using the Splus2000 or Splus6 statistical package.
5. Prognosis Signature of 295 Breast Cancers
From each of the 295 tumors, total RNA was isolated and used to generate cRNA, which was labeled and hybridized to microarrays containing ˜25,000 human genes (see Materials and Methods). Fluorescence intensities of scanned images were quantified and normalized to yield the transcript abundance of a gene as an intensity ratio as compared to a reference pool of cRNA made up of equal amounts of cRNA of all tumors combined. The gene expression ratios of the previously determined 70 prognosis marker genes for all 295 tumors in this study are shown in
Table 9 summarizes the association between the prognosis profile and clinical parameters, which reveals that the prognosis profile is associated with histological grade, ER status and age, but not significantly with tumor diameter, vascular invasion, number of positive lymph nodes, or with treatment.
6. Prognostic Value of Gene Expression Signature
Distant metastasis-free probability and overall survival were calculated for all patients having tumors with either a “good” or “poor prognosis” signature (
The prognosis profile was first identified within a selected group of lymph node negative patients. Here, we wished to determine the performance of the prognostic signatures in both lymph node negative and positive patients. In the series of 151 lymph node negative patients (of the 295 patient cohort), the prognosis profile performed extremely well in predicting outcome of disease (
7. Multivariable Analysis
Results from the multivariable analysis of distant metastases as first event including age, diameter, number of positive nodes, grade, vascular invasion, ER expression, treatment and the gene expression profile are shown in Table 11. The only independent predictive factors were the 70 gene expression profile, tumor diameter and adjuvant chemotherapy. During the period in which these patients were treated, the majority of premenopausal lymph node positive patients received adjuvant chemotherapy; lymph node negative patients usually did not receive adjuvant treatment. There was improved survival for patients who received adjuvant chemotherapy in this series of tumors. The 70 gene expression profile is by far the strongest predictor for distant metastases with an overall hazard ratio of 4.6 (95% CI: 2.3–9.2; p<0.0001). This is not unexpected, since the prognosis profile was established based on tumors from patients that all developed distant metastases within five years.
The prognosis profile is also a strong predictor of developing distant metastases within the group of lymph node positive patients (see
A key question is whether the prognosis profile is a more useful clinical tool to determine eligibility for adjuvant systemic treatment than the presently used “St. Gallen” and “NIH-consensus” criteria, which are based on histological and clinical characteristics (see Goldhirsch et al., Meeting Highlights: International Consensus Panel on the Treatment of Primary Breast Cancer, Seventh International Conference on Adjuvant Therapy of Primary Breast Cancer, J. Clin. Oncol. 19(18):3817–3827 (2001); Eifel et al., National Institutes of Health Consensus Development Conference Statement: Adjuvant Therapy for Breast Cancer, Nov. 1–3, 2000, J. Natl. Cancer Inst. 93(13):979–989 (2001)).
Two major conclusions can be drawn from this comparison. First, the prognosis profile assigns many more pN0 patients to the low-risk group than the traditional methods (38% for “profile”, versus 15% for “St. Gallen” and 7% for “NIH consensus”). Second, low-risk patients identified by expression profiling have better metastasis-free survival than those classified by “St. Gallen” or “NIH consensus” criteria. Conversely, patients classified as high-risk according to their expression profile tend to develop distant metastases more often than the high risk “St. Gallen” or “NIH consensus” patients. This indicates that both “St. Gallen” and “NIH” criteria misclassify a significant number of patients. Indeed, the high-risk group as defined by “NIH consensus” criteria contains a significant number of patients having a “good prognosis” signature and corresponding outcome (
Tumor size is a major parameter used in the “NIH-consensus” criteria for adjuvant therapy selection. However, the data above (see Table 9) show that the ability to develop distant metastases is only partially dependent on tumor size and suggest that metastatic capacity in many tumors is an early and inherent genetic property.
The “good prognosis groups” can be subdivided into two groups whose treatment regimens differ. The subgroups were determined by using another threshold in the correlation with the average profile of the good prognosis tumors. In the initial study that identified markers correlated with a good prognosis (see Example 4), we found that tumors having a correlation coefficient of greater than 0.636 (i.e., whose expression profiles correlated most strongly with the average expression profile of the “good prognosis” group) did not give rise to distant metastases. This was determined empirically for the 78 patient tumor samples by determining the correlation coefficient, in the ranked list, above which patients developed no distant metastases (data not shown). Thus, among the tumors previously identified as having a “good prognosis” signature, those that had a correlation coefficient exceeding 0.636 were classified as having a “very good prognosis” signature. These patients with such a “very good prognosis” signature in their tumor (
Together, our data indicate that the prognosis profile is a more accurate tool to select lymph node negative premenopausal patients for adjuvant systemic therapy than the presently used consensus criteria and may even be useful to guide adjuvant therapy in lymph node positive patients. We propose the following treatment regimens based upon the particular marker expression signature:
(1) Lymph node negative patients having a tumor with a “very good prognosis” signature can be treated without adjuvant systemic therapy.
(2) Lymph node negative patients having a tumor with an “intermediate prognosis” signature can be treated with adjuvant hormonal therapy only. As 97% of tumors having the “intermediate prognosis” signature are ER positive, this group of patients should benefit from adjuvant hormonal treatment. Adding chemotherapy to the treatment regimen of this patient group would result in only marginal survival benefit.
(3) All other patients should receive adjuvant chemotherapy. Where the tumor is ER+, hormonal therapy is also recommended.
Implementation of the use of the prognostic profile in breast cancer diagnostics should result in improved and patient-tailored adjuvant systemic treatment, reducing both over- and undertreatment.
7. REFERENCES CITEDAll references cited herein are incorporated herein by reference in their entirety and for all purposes to the same extent as if each individual publication or patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety for all purposes.
Many modifications and variations of the present invention can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. The specific embodiments described herein are offered by way of example only, and the invention is to be limited only by the terms of the appended claims along with the full scope of equivalents to which such claims are entitled.
Claims
1. A method of assigning treatment to a breast cancer patient, wherein said breast cancer patient is a human breast cancer patient, comprising:
- (a) classifying said breast cancer patient as having a prognosis selected from the group consisting of a first prognosis, a second prognosis, and a third prognosis on the basis of a first expression profile comprising the nucleic acid levels of expression of at least five genes listed in Table 5 in a clinically relevant cell sample from said breast cancer patient by a method comprising (a1) determining the similarity between said first expression profile and a first good prognosis expression profile comprising the nucleic acid levels of expression of said at least five genes to obtain a patient similarity value, wherein said nucleic acid levels of expression in said first good prognosis expression profile represent the nucleic acid levels of expression of said genes in patients having no distant metastases within five years of initial diagnosis; and (a2) classifying said breast cancer patient as having said first prognosis if said patient similarity value exceeds a second similarity threshold value, said second prognosis if said patient similarity value exceeds a first similarity threshold value but does not exceed said second similarity threshold value, and said third prognosis if said patient similarity value does not exceed said first similarity threshold value, wherein said second similarity threshold indicates greater similarity to said first good prognosis expression profile than does said first similarity threshold; and
- (b) assigning said breast cancer patient a treatment without adjuvant chemotherapy if the breast cancer patient is lymph node negative and is classified as having said first prognosis or said second prognosis, or assigning said breast cancer patient a treatment with adjuvant chemotherapy if said breast cancer patient (b1) is lymph node positive and is classified as having said first prognosis, said second prognosis, or said third prognosis, or (b2) is lymph node negative and is classified as having said third prognosis.
2. The method of claim 1, further comprising determining prior to step (a1) said nucleic acid levels of expression of said at least five genes in said clinically relevant cell sample.
3. The method of claim 1, wherein said patient similarity value is a correlation coefficient between said first expression profile and said good prognosis expression profile, and wherein said determining in step (a1) is carried out by a method comprising determining said correlation coefficient.
4. The method of claim 1, wherein said at least five genes are at least 10 genes listed in Table 5.
5. The method of claim 1, wherein said at least five genes are at least 20 genes listed in Table 5.
6. The method of claim 1, wherein said at least five genes are at least 30 genes listed in Table 5.
7. The method of claim 1, wherein said at least five genes are at least 40 genes listed in Table 5.
8. The method of claim 1, wherein said at least five genes are at least 50 genes listed in Table 5.
9. The method of claim 1, wherein said at least five genes are all genes listed in Table 6.
10. The method of claim 1, wherein said first threshold similarity value and said second threshold similarity value are selected by a method comprising:
- (i) rank ordering in descending order a plurality of breast cancer tumor samples, each obtained from a training breast cancer patient of a plurality of training breast cancer patients whose clinical and follow-up data are available for at least five years after initial diagnosis, according to the degree of similarity between a second expression profile comprising the nucleic acid levels of expression of said at least five genes in each of said tumor samples and a second good prognosis expression profile, wherein said second good prognosis expression profile comprises the mean nucleic acid level of expression of each of said at least five genes in tumor samples from training breast cancer patients in said plurality of training breast cancer patients who have no distant metastases within five years of initial diagnosis, to obtain a rank-ordered list, said degree of similarity being expressed as a similarity value;
- (ii) selecting a particular number or percentage of false negatives for classifying said plurality of training breast cancer patients, wherein a false negative is a training breast cancer patient for whom the second expression profile predicts that said training breast cancer patient will have no distant metastases within the first five years after initial diagnosis, but who has had a distant metastasis within five years after initial diagnosis;
- (iii) determining a threshold similarity value, wherein training breast cancer patients from whom tumor samples having a similarity value above said threshold similarity value in said rank ordered list are predicted as having no distant metastases within the first five years after initial diagnosis, and wherein among said training breast cancer patients predicted as having no distant metastases the number or percentage of training breast cancer patients who are false negatives is less than said particular number or percentage selected in said step (ii);
- (iv) selecting said threshold similarity value determined in step (iii) as said first threshold similarity value; and
- (v) selecting a second similarity value by a method comprising determining which of the tumor samples taken from breast cancer patients having a distant metastasis within five years after initial diagnosis in said rank ordered list has the greatest similarity value, and selecting said greatest similarity value as said second threshold similarity value.
11. The method of claim 9, wherein said first expression profile comprises log intensity values for each of the respective genes listed in Table 6, and said first good prognosis profile comprises the mean log intensity values that are listed in Table 7, and wherein said first threshold similarity value is 0.4 and said second threshold similarity value is 0.636.
12. The method of claim 1, wherein said nucleic acid level of expression of each gene of said at least five genes is a relative nucleic acid level of expression of said gene in said cell sample versus the nucleic acid level of expression of said gene in a reference pool.
13. The method of claim 1, wherein said step (a) further comprises before said step (a2):
- (i) rank ordering in descending order a plurality of breast cancer tumor samples, each obtained from a training breast cancer patient of a plurality of training breast cancer patients for whom clinical and follow-up data are available for at least five years after initial diagnosis, according to the degree of similarity between a second expression profile comprising the nucleic acid levels of expression of said at least five genes in each of said tumor samples and a second good prognosis expression profile comprising the mean nucleic acid level of expression of each of said at least five genes in tumor samples from training breast cancer patients in said plurality of training breast cancer patients who have no distant metastases within five years of initial diagnosis, to obtain a rank-ordered list, said degree of similarity being expressed as a similarity value;
- (ii) selecting a particular number or percentage of false negatives for classifying said plurality of training breast cancer patients, wherein a false negative is a training breast cancer patient for whom the second expression profile comprising the nucleic acid levels of said at least five genes predicts that said training breast cancer patient will have no distant metastases within the first five years after initial diagnosis, but who has had a distant metastasis within five years after initial diagnosis;
- (iii) determining a threshold similarity value, wherein training breast cancer patients whose tumor samples having a similarity value above said threshold similarity value in said rank ordered list are predicted as having no distant metastases within the first five years after initial diagnosis, and wherein among said training breast cancer patients predicted as having no distant metastases the number or percentage of training breast cancer patients who are false negatives is less than said particular number or percentage selected in said step (ii);
- (iv) selecting said threshold similarity value determined in step (iii) as said first threshold similarity value; and
- (v) selecting a second similarity value by a method comprising determining which of the tumor samples taken from breast cancer patients having a distant metastasis within five years after initial diagnosis in said rank ordered list has the greatest similarity value, and selecting said greatest similarity value as said second threshold similarity value.
14. A method of assigning a treatment to a breast cancer patient, wherein said breast cancer patient is a human breast cancer patient, comprising:
- (a) determining the lymph node status for said breast cancer patient;
- (b) determining the nucleic acid levels of expression of at least five genes listed in Table 5 in a clinically relevant cell sample from said breast cancer patient, thereby generating a first expression profile comprising the nucleic acid levels of expression of said at least five genes in said cell sample;
- (c) classifying said breast cancer patient as having a prognosis selected from the group consisting of a first prognosis, a second prognosis, and a third prognosis by a method comprising (c1) determining the similarity between said first expression profile and a first good prognosis expression profile comprising the nucleic acid levels of expression of said at least five genes to obtain a patient similarity value, wherein said nucleic acid levels of expression in said good prognosis expression profile represent the nucleic acid levels of expression of said genes in breast cancer patients having no distant metastases within five years of initial diagnosis; and (c2) classifying said breast cancer patient as having said first prognosis if said patient similarity value exceeds a second similarity threshold value, said second prognosis if said patient similarity value exceeds a first similarity threshold value but does not exceed said second similarity threshold value, and said third prognosis if said patient similarity value does not exceed said first similarity threshold value, wherein said second similarity threshold value indicates greater similarity to said first good prognosis expression profile than does said first similarity threshold value; and
- (d) assigning said breast cancer patient a treatment without adjuvant chemotherapy if the breast cancer patient is lymph node negative and is classified as having said first prognosis or said second prognosis, or assigning said breast cancer patient a treatment with adjuvant chemotherapy if said breast cancer patient (d1) is lymph node positive and is classified as having said first prognosis, said second prognosis, or said third prognosis, or (d2) is lymph node negative and is classified as having said third prognosis.
15. The method of claim 14, in which said treatment without adjuvant chemotherapy comprises adjuvant hormonal therapy if said breast cancer patient is lymph node negative and is classified as having said second prognosis.
16. The method of claim 14, wherein said classifying step (c) further comprises before said step (c2):
- (i) rank ordering in descending order a plurality of breast cancer tumor samples, each obtained from a different training breast cancer patient of a plurality of training breast cancer patients for whom clinical and follow-up data are available for at least five years after initial diagnosis, according to the degree of similarity between a second expression profile comprising the nucleic acid levels of expression of said at least five genes in each of said tumor samples and a second good prognosis expression profile, said second good prognosis expression profile comprising the nucleic acid levels of expression of said genes that represent the nucleic acid levels of expression of said genes in tumor samples from training breast cancer patients in said plurality of training breast cancer patients who have no distant metastases within five years of initial diagnosis, said degree of similarity being expressed as a similarity value;
- (ii) selecting a particular number or percentage of false negatives for classifying said plurality of training breast cancer patients, wherein a false negative is a training breast cancer patient for whom the second expression profile predicts that said training breast cancer patient will have no distant metastases within the first five years after initial diagnosis, but who has had a distant metastasis within the first five years after initial diagnosis;
- (iii) determining a threshold similarity value, wherein training breast cancer patients whose tumor samples having a similarity value above said threshold similarity value in said rank ordered list are predicted as having no distant metastases within the first five years after initial diagnosis, and wherein among said training breast cancer patients predicted as having no distant metastases the number or percentage of training breast cancer patients who are false negatives is not greater than said particular number or percentage selected in step (ii);
- (iv) selecting said threshold similarity value determined in step (iii) as said first threshold similarity value; and
- (v) selecting a second similarity value, greater than said first similarity value, as said second threshold similarity value.
17. The method of claim 14 which further comprises determining the estrogen receptor (ER) status of said breast cancer patient, wherein if said breast cancer patient is ER positive and lymph node negative, said treatment assigned to said breast cancer patient additionally comprises adjuvant hormonal therapy.
18. The method of claim 14, wherein said breast cancer patient is 52 years of age or younger.
19. The method of claim 14 or 18, wherein said breast cancer patient has stage I or stage II breast cancer.
20. The method of claim 14, wherein said breast cancer patient is premenopausal.
21. The method of claim 14, wherein said patient similarity value is a correlation coefficient between said first expression profile and said first good prognosis expression profile, and wherein said determining in step (c1) is carried out by a method comprising determining said correlation coefficient.
22. The method of claim 14, wherein said at least five genes are at least 10 genes listed in Table 5.
23. The method of claim 14, wherein said at least five genes are at least 20 genes listed in Table 5.
24. The method of claim 14, wherein said at least five genes are at least 30 genes listed in Table 5.
25. The method of claim 14, wherein said at least five genes are at least 40 genes listed in Table 5.
26. The method of claim 14, wherein said at least five genes are at least 50 genes listed in Table 5.
27. The method of claim 14, wherein said at least five genes are all genes listed in Table 6.
28. The method of claim 14, wherein said first threshold similarity value and said second threshold similarity value are selected by a method comprising:
- (i) rank ordering in descending order a plurality of breast cancer tumor samples, each obtained from a training breast cancer patient of a plurality of training breast cancer patients for whom clinical and follow-up data are available for at least five years after initial diagnosis by the degree of similarity between a second expression profile comprising the nucleic acid levels of expression of said at least five genes in each of said tumor samples and a second good prognosis expression profile, wherein said second good prognosis expression profile comprises the mean nucleic acid level of expression of each of said at least five genes in tumor samples from training breast cancer patients in said plurality of training breast cancer patients who have no distant metastases within five years of initial diagnosis, to obtain a rank-ordered list, said degree of similarity being expressed as a similarity value;
- (ii) selecting a particular number or percentage of false negatives for classifying said plurality of training breast cancer patients, wherein a false negative is a training breast cancer patient for whom the second expression profile comprising the nucleic acid levels of said at least five genes in said cell sample predicts that said training breast cancer patient will have no distant metastases within the first five years after initial diagnosis, but who has had a distant metastasis within the first five years after initial diagnosis;
- (iii) determining a threshold similarity value, wherein training breast cancer patients whose tumor samples having a similarity value above said threshold similarity value in said rank ordered list are predicted as having no distant metastases within the first five years after initial diagnosis, and wherein among said training breast cancer patients predicted as having no distant metastases the number or percentage of training breast cancer patients who are false negatives is less than said particular number or percentage selected in said step (ii);
- (iv) selecting said threshold similarity value determined in step (iii) as said first threshold similarity value; and
- (v) selecting a second similarity value by a method comprising determining which of the tumor samples taken from breast cancer patients having a distant metastasis within the first five years after initial diagnosis in said rank ordered list has the greatest similarity value, and selecting said greatest similarity value as said second threshold similarity value.
29. The method of claim 27, wherein said first expression profile comprises log intensity values for each of the respective genes listed in Table 6, and said first good prognosis profile comprises the mean log intensity values that are listed in Table 7, and wherein said first threshold similarity value is 0.4 and said second threshold similarity value is 0.636.
30. The method of claim 14, wherein said nucleic acid level of expression of each gene of said at least five genes in said first expression profile is a relative nucleic acid level of expression of said gene in said cell sample versus the nucleic acid level of expression of said gene in a reference pool.
31. The method of claim 12 or 30, wherein said reference pool is derived from a normal breast cell line.
32. The method of claim 12 or 30, wherein said reference pool is derived from a breast cancer cell line.
33. The method of claim 12 or 30, wherein said relative nucleic acid level of expression is represented as a log ratio.
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Type: Grant
Filed: Jan 15, 2003
Date of Patent: Jan 30, 2007
Patent Publication Number: 20040058340
Assignees: Rosetta Inpharmatics LLC (Seattle, WA), Netherlands Cancer Institute (Amsterdam)
Inventors: Hongyue Dai (Bothell, WA), Yudong He (Kirkland, WA), Peter S. Linsley (Seattle, WA), Mao Mao (Kirkland, WA), Christopher J. Roberts (Seattle, WA), Laura Johanna Van't Veer (Amsterdam), Marc J. Van de Vijver (Amsterdam), Rene Bernards (Abcoude), A. A. M. Hart (Castricum)
Primary Examiner: John S. Brusca
Assistant Examiner: Shobo (Joe) Zhou
Attorney: Jones Day
Application Number: 10/342,887
International Classification: G01N 33/48 (20060101); C12Q 1/68 (20060101);
